Efficient Object Detection

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Overview

Efficient object detection studies how to find and locate objects accurately while using limited computation, memory, time, and energy, especially on mobile and edge devices. Common approaches include compact YOLO and real-time DETR models, streamlined feature pyramids, pruning, quantization and teacher-to-student distillation, as well as sparse or adaptive processing that focuses computation on likely objects or informative frames. Work is tested on benchmarks such as COCO for general detection and VisDrone for aerial small objects, alongside measurements on hardware such as phones, embedded boards and FPGAs. The central challenge is balancing accuracy—particularly for tiny or difficult objects—with speed and energy use, while ensuring benchmark gains translate to reliable performance on real devices and varied scenes.

Latest papers

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95 papers9 papers in the last four weeks, down 31% on the four weeks before. 0.1% of all new papers.

Aug 16, 2026

YOLO26-RD: An End-to-End Road Damage Detection Network With Learnable Contrast Enhancement and Edge-Guided Downsampling

Pavement distress detectors are conventionally specialised for small objects, typically by adding a stride-4 detection head and replacing strided convolution with space-to-depth downsampling. This paper tests that premise against the annotation geometry of region level survey imagery and finds it fails: 1.28% of instances are small at 640 resolution while 70.37% are large, yet a stride-4 level would claim 75.3% of anchors, and complete misses rather than localisation errors dominate baseline failures. YOLO26-RD therefore reallocates the anchor budget, retaining the stride-4 branch as neck features but carrying no detection level there, and adds LearnableContrast, a 494 parameter per tile correction learned from the detection loss and active at inference, and EdgeSPD, a lossless space-to-depth downsampler gated by a fixed Sobel prior. Fifteen models were trained from scratch under one recipe, five scales each of YOLO26-RD and of matched YOLO26 and YOLOv12 families. Averaged over scales YOLO26-RD returns 0.790 mAP50 and 0.482 mAP50-95 against 0.776 and 0.471 for YOLO26 and 0.755 and 0.468 for YOLOv12; it exceeds both on mAP50 at every scale from s upward, and at m, l and x it leads on both metrics, twelve pairwise comparisons decided without exception. YOLO26-RD-l is the best of the fifteen at 0.809 mAP50 and 0.497 mAP50-95, improving on the YOLO26 reference by 0.031 and 0.030 and leading all six per class entries; every arm of a module ablation also exceeds that reference. The margin is thus a property of the architecture rather than of one tuned configuration, though three of the twelve margins lie inside the dataset 0.015 resolution limit and the held out split reproduces the ordering against YOLO26 but not YOLOv12 at scale x. As a TensorRT FP16 engine the released model sustains 98 frames per second on an entry level accelerator, against the 21 needed at 100 km/h.

Sompote Youwai, Pawarotorn Chaipetch, Hathairat Samaikul +1

AI Research Group, Department of Civil Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand · Infraplus Co., Ltd., Thailand

Object DetectionReal-Time Object DetectionEfficient Object Detection
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Aug 15, 2026

SPEANet: Structural Prior Enhanced Attention Network for Parameter-Efficient Remote Sensing Object Detection

Remote sensing object detection (RSOD) requires compact backbones capable of preserving weak geometric cues under extreme scale variation and background clutter. Fixed structural operators provide complementary contour and frequency responses without introducing learnable operator coefficients. However, directly injecting these responses can amplify content-irrelevant textures, while applying a uniform operator design across the hierarchy may be poorly matched to stage-specific representation requirements. We propose the Structural Prior Enhanced Attention Network (SPEANet), a parameter-efficient RSOD backbone that integrates fixed operators through stage-specific prior extraction and context-conditioned response modulation. SPEANet assigns smoothed contour and multi-order directional modeling to shallow, high-resolution features, while employing a compact approximation-detail interaction mechanism in deeper stages. Learned spatial gates regulate the resulting prior responses before residual fusion. Experiments on five benchmarks, together with evaluations across seven detection frameworks on DOTA-v1.0, achieve a favorable accuracy-parameter trade-off. With Oriented R-CNN, SPEANet achieves 78.55% mAP on DOTA-v1.0, 72.24% mAP on DOTA-v1.5, and 67.30% mAP on DIOR-R using 23.0M total parameters, including a 5.97M-parameter backbone.

Wei Lu, Junjie Li, Feifei Sang +1

MOE Key Lab of ICSP, IMIS Lab of Anhui, Anhui Provincial Key Lab of Multimodal Cognitive Computation, Zenmorn-AHU · AI Joint Lab, School of Computer Science and Technology, Anhui University, Hefei 230601, China

Efficient Object DetectionAerial Object DetectionConvolutional Neural NetworksRemote Sensing Small Object Detection
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Aug 7, 2026

SkySeaLand: A Wide-Format Satellite Transportation Benchmark with an Ultra-Lightweight Detection Baseline

Satellite object detection is challenged by small targets and wide-format scenes that lose detail under standard square-input resizing. We introduce SkySeaLand, a public dataset of 1,307 high-resolution satellite images and 19,101 verified bounding boxes across airplane, boat, car, and ship classes in terrestrial and maritime scenes. Native COCO and YOLO annotations are provided. The collection is dominated by large source images and wide scene geometry: 84.5 percent exceed 3,836 pixels on the longest side and 73.1 percent are near a 3:1 aspect ratio. We evaluate twelve detectors from the YOLO, RT-DETR, DETR, and Faster R-CNN families using a common split and COCO metrics. The tested YOLO and RT-DETR variants obtain 84.4--88.2 mAP50, with no consistent accuracy gain from larger parameter counts under the reported model-specific recipes. We also report SkyDet, a 1.22 M parameter anchor-free baseline that obtains 60.5 mAP50 and 24.32 mAP50-95 in a 4.90 MB footprint, with 13.74 ms latency (72.8 FPS) on a Tesla T4. SkySeaLand provides a compact benchmark for mixed land--maritime transportation detection, while SkyDet establishes a documented low-footprint reference rather than a state-of-the-art accuracy claim.

Md. Zahid Hasan Riad, Md Sultanul Islam Ovi

Dept. of Computer Science and Engineering, Green University of Bangladesh, Bangladesh · Dept. of Computer Science, George Mason University, Virginia, USA

Remote Sensing Small Object DetectionSmall Object DetectionEfficient Object DetectionObject DetectionRemote Sensing Benchmarks
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Aug 6, 2026

CFGPNet: Cross-Attention-Based Fused Gradient Programmed Network Framework for Multispectral Object Detection

Multispectral object detection combines visible and thermal imagery to improve perception under challenging illumination and environmental conditions. However, differences in modality appearance and reliability can introduce redundant or conflicting responses, limiting the use of complementary information. Complex fusion mechanisms further increase computational cost, creating a persistent trade-off between detection accuracy and efficiency. To address these challenges, CFGPNet is proposed, a cross-attention-based fused gradient programmed network. The framework incorporates re-parameterized RepViT blocks into the YOLOv9 architecture to strengthen spatial and channel representations while maintaining efficient feature extraction. Cross Computation Efficient Attention (CrossCEA) exchanges spatial attention maps between modalities at multiple detection scales, allowing each stream to emphasize regions supported by the other while preserving modality-specific information. Attention Selection and Aggregation Fusion (ASAF) combines dense feature aggregation with selection of the strongest responses from multiple attention branches to form compact, discriminative fused representations. A programmable gradient information pathway provides auxiliary supervision during training to improve feature learning. This pathway is removed after training, adding no parameters or operations at inference. Experiments on FLIR, M3FD, LLVIP, VEDAI, and MFAD demonstrate favorable accuracy-efficiency trade-offs across three model scales, with the smallest variant requiring 15.3 million parameters and 56.9 GFLOPs. The code is available at https://github.com/NimaHatami99/CFGPNet.

Nima Hatami, Karim Faez, Saeed Sharifian +1

Department of Electrical Engineering, Amirkabir University of Technology, 15914, Tehran, Iran

Multispectral Object DetectionEfficient Object DetectionCross-Modal AttentionObject DetectionMultimodal Fusion
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Aug 5, 2026

YOLOv14: Adaptive Real-Time Object Detection for Diverse Imaging Conditions

Real-time object detectors achieve remarkable accuracy under controlled conditions, yet degrade sharply on non-ideal inputs: fisheye distortion, game-renderedcharacters, aerial viewpoints, and 360°panoramas. We present YOLOv14, a detection framework with four adaptive mechanisms designed for specific types of inputvariation:(1) Deformable Area-Attention with windowed computation and shiftedwindows for geometric distortion;(2) Multi-level Game2Real Alignment with progressive adversarial training for domain shift;(3) View-Aware Contrastive Learning with adaptive temperature for viewpoint invariance; and (4) Scene-Adaptive Augmentation with dynamic loss balancing for scene diversity. Together, YOLOv14 achieves 49.1 mAP on COCO val2017 at 2.91 ms (T4 GPU), and delivers substantial gains on fisheye (+4.1 mAP), panorama (+6.6 mAP), drone (+6.4 mAP), andour synthesized game-character benchmark (+26.1 mAP). We release code and models to facilitate reproducible research.

Jinling Jia, Jian Lu, Jone Yawl +1

Nanjing University of Posts and Telecommunications

Domain AdaptationEfficient Object DetectionObject DetectionReal-Time Object Detection
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Aug 1, 2026

RadYOLO: Computationally Efficient 3D Object Detection and Segmentation in CT and MRI

Object detection and segmentation in three-dimensional medical images is a very active area of research. However, most proposed deep learning models carry a high computational cost, and only few aim to be broadly applicable, achieve high detection performance, and remain fast to execute on resource-constrained hardware. To address this gap, we present RadYOLO, a 3D extension of YOLO11 tailored to medical images. We compare it with nnU-Net and nnDetection on five datasets comprising CT and MRI data with varying object sizes and prevalence. RadYOLO's detection performance surpasses that of nnDetection on four of five datasets and is comparable on one. Compared to nnU-Net, RadYOLO performs better on lesion detection tasks, while nnU-Net excels at detecting large organs when precise localization is required. When rough object localization is sufficient, RadYOLO matches or outperforms nnU-Net on all five datasets. Regarding inference time, RadYOLO is 8-46x faster than nnU-Net on a GPU. Compared to nnDetection the speedup is even higher. When executed on a CPU, RadYOLO's inference runs within seconds (still faster than nnU-Net on a GPU) offering a significant advantage for clinical and edge-device deployment. RadYOLO repository: https://github.com/FraunhoferMEVIS/RadYOLO

Kai Geissler, Laurens Müller-Groh, Hans Meine

Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Str. 2, 28359 Bremen, Germany

3D Object Detection3D Medical ImagingEfficient Object DetectionReal-Time Object Detection3D Medical Image SegmentationObject Detection+1
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Jul 14, 2026

MambaPSA: A Mamba-based Replacement for C2PSA in YOLO26

State space models (SSMs), notably Mamba, have recently emerged as efficient alternatives to self-attention with linear computational complexity. We investigate the integration of Mamba into YOLO26, the latest non-maximum suppression (NMS)-free object detection framework, by proposing MambaPSA, a lightweight Mamba-based replacement for the C2PSA block at the end of the backbone. To complement this study, we additionally insert a bidirectional Vision Mamba (BiViM) module at the P3, P4, and P5 levels of the neck. Experiments on PASCAL VOC 2007+2012 show that MambaPSA reduces parameters by 2.9%, FLOPs by 12.1%, and improves CPU inference throughput by 17.6% (from 17 to 20 FPS) with negligible accuracy change (-0.1 mAP50:95), while the P4 BiViM placement yields the best accuracy gain (+0.9 mAP50:95). These results suggest that SSMs offer a favorable efficiency-accuracy trade-off when replacing attention-based blocks in NMS-free lightweight detectors.

Sheng-Wei Chan, Chia-Min Lin, Hsin-Jui Pan +4

Department of Electrical and Computer Engineering, Tamkang University, New Taipei City, Taiwan

Efficient Neural Network InferenceEfficient Object DetectionObject DetectionState Space Models
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Jul 13, 2026

MicroCharNet: Less is More for License Plate Character Detection

License plate character detection is a crucial component of intelligent transportation systems, where high accuracy and computational efficiency are required for real-time deployment. Although recent deep learning-based methods have substantially improved detection performance, many high-accuracy models rely on large-scale architectures that incur substantial computational overhead, limiting their applicability to resource-constrained devices. In this paper, we propose MicroCharNet, an ultra-lightweight model specifically designed for license plate character detection. The proposed architecture employs a compact backbone composed of C2f blocks, integrated with CoordAtt module to enhance feature extraction while preserving spatial information. A lightweight C3k2-based neck fuses multi-level features, followed by a single-level anchor-free detection head that enables end-to-end prediction. Experiments conducted on the UFPR-ALPR dataset demonstrate that MicroCharNet achieves competitive detection accuracy with only 0.08M parameters and 0.096 GFLOPs, while outperforming several recent YOLO-based baselines. Hardware-level evaluations further confirm its efficiency for real-time deployment on edge devices. These results indicate that carefully designed ultra-lightweight architectures can effectively balance accuracy and efficiency in license plate character detection. The source code is available at https://github.com/chequanghuy/MicroCharNet.

Huy Che, Dinh-Duy Phan, Duc-Lung Vu

University of Information Technology, Ho Chi Minh City, Vietnam · 2Vietnam National University, Ho Chi Minh City, Vietnam

Edge InferenceEfficient Object DetectionObject DetectionAutomatic License Plate Recognition
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Jul 5, 2026

FRFDet: Efficient UAV Small Object Detection with Symmetric Sampling and Scalable Fusion

Small object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging under adverse conditions, including complex weather, low illumination, and sensor noise. These challenges mainly stem from severe background clutter, fine-grained detail degradation, and suboptimal semantic-spatial feature fusion, which jointly hinder robust small-object representation. To this end, we propose FRFDet, a lightweight yet effective single-stage detector tailored for UAV-based small object detection. FRFDet proposes two plug-and-play modules: Inverse Bidirectional Sampling (IBS) and Scale-Feature Relationship Cross-Fusion (SFRCF). IBS preserves critical spatial details via channel expansion-compression and bidirectional pattern reconstruction, improving feature alignment. SFRCF explicitly models scale-dependent fusion behaviors, revealing that inter-group element-wise multiplication favors compact models, while inter-group additive fusion benefits larger architectures. Extensive experiments on VisDrone, UAVDT, HazyDet, and MS COCO demonstrate that FRFDet achieves state-of-the-art performance among lightweight detectors with low computational cost, compact parameters, and fast inference, making it well suited for resource-constrained UAV platforms.

Yunzhong Si, Huiying Xu, Xinzhong Zhu +4

College of Computer Science and Technology, Zhejiang Normal University, Jinhua, China · Hangzhou School of Automation, Zhejiang Normal University, Hangzhou, China · Beijing Geekplus Technology Co., Ltd, Beijing, China

Remote Sensing Small Object DetectionSmall Object DetectionEfficient Object DetectionMulti-Scale Feature FusionObject DetectionAerial Object Detection
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Jun 30, 2026

Real-Time Source-Free Object Detection

Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet existing source-free object detection (SFOD) methods rely on heavyweight architectures that prioritize accuracy alone. We show this trade-off is unnecessary: building on YOLOv10, an NMS-free dual-head detector, we achieve state-of-the-art adaptation accuracy while being faster and more compact. We observe that directly applying vanilla mean-teacher self-training to dual-head detectors leads to suboptimal adaptation performance due to two key factors. First, simple pseudo-label generation strategies, such as using a single head or directly combining high-confidence predictions from both heads, yield suboptimal supervision under domain-shift. We propose DHF (Dual-Head Pseudo-Label Fusion) which selectively admits one-to-one (O2O) and one-to-many (O2M) head predictions, preserving precision and recovering missed objects. Second, we observe domain-shift collapses multi-scale feature discriminability. We propose the use of our MARD (Multi-scale Adaptive Representation Diversification) loss which mitigates this by enforcing detection-aware variance and covariance constraints on multi-scale feature maps. Both modules are training-time only, leaving inference unchanged. Across domain-shift benchmarks, our method, RT-SFOD yields 1.4 to 3.5% mAP gains, 1.3×\times higher throughput, with ∼\sim2×\times fewer parameters than prior state-of-the-art SFOD methods, thus advancing the Pareto frontier of the speed-accuracy-model size trade-off. We report main results with YOLOv10, and demonstrate generalizability with additional YOLO- and DETR-based dual-head detectors. Code is available here: https://github.com/Sairam13001/RT-SFOD/

Sairam VCR, Varun Gopal, Poornima Jain +2

1IIT Hyderabad, India · 2Microsoft Research · 3MBZUAI

Source-Free Domain AdaptationSelf-TrainingEfficient Object DetectionObject DetectionReal-Time Object Detection
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Jun 29, 2026

Efficient RGB-T Object Detection via Sparse Cross-Modality Fusion

RGB-T detectors leverage the complementary strengths of visible and thermal infrared modalities, achieving robust performance under challenging conditions. Many of them resort to heavy dual backbones and exhaustive cross-modality fusion across the entire image, leading to impractically high computational costs. We observe that most image regions are smooth backgrounds (e.g., sky, ground) that can be easily handled by lightweight single-modality models. In light of this observation, we propose a sparse fusion mechanism for efficient RGB-T detection: first rapidly scanning the image to identify the proposals and then carefully examining the remaining sparse proposals via feature fusion. We propose a two-stage framework to instantiate this mechanism, which performs detection in two stages: 1) a lightweight and modality-specific detection stage that produces high-recall RoIs, and 2) a fusion-driven examination and refinement stage that filters out the false positives and refines the bounding boxes. This design enables the detector to adaptively allocate more computational resources to the potential foregrounds, improving the efficiency while ensuring detection accuracy. Extensive experiments show that our method achieves competitive performance with substantially fewer parameters and lower cost, while maintaining strong scalability to high-resolution images.

Chao Tian, Zikun Zhou, Chao Yang +2

Harbin Institute of Technology, Shenzhen, Guangdong, China

Multispectral Object DetectionEfficient Object DetectionObject DetectionMultimodal Fusion
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Jun 22, 2026

From Spatial to Spectral: An Efficient, Frequency-Guided Feature Representation Learner for Small Object Detection

Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequency details. Recovering these fragile cues within the spatial domain is notoriously difficult, as it often requires computationally expensive architectural upscaling that inadvertently amplifies background noise. To bridge this gap, we propose a paradigm shift from spatial to spectral feature processing, introducing a holistic solution with the following novelty: (1) A versatile Frequency-Guided Feature Representation framework that generalizes across diverse detector architectures (both CNN and Transformer-based), offering a robust alternative to spatial-only feature extraction; (2) The unified Decompose--Enhance--Reconstruct (DER) operator, instantiated via three lightweight, plug-and-play modules -- Wavelet-Difference Gate (WDG), Log-Gabor Enhancer (LGE), and Frequency-Driven Head (FDHead) -- to systematically inject frequency-aware modulation into the backbone, neck, and head. This mechanism decouples feature modeling from resolution reduction, capturing discriminative high-frequency components to enable accurate localization with significantly reduced parameter redundancy; (3) Extensive validation on multi-domain benchmarks (VisDrone2019, UAVDT, TinyPerson, DOTAv1) demonstrating consistent gains. Notably, our proposed DERNet series outperforms YOLOv11 models under the same scale while requiring only 1/6 of the parameters, backed by rigorous spectral diagnostics and error decomposition analysis.

Yuhan Rui, Shihan Qiao, Yibin Lou +7

Southern University of Science and Technology, Shenzhen, China · University of Pennsylvania

Frequency-Domain Feature LearningSmall Object DetectionEfficient Object DetectionObject DetectionAerial Object Detection
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Jun 2, 2026

Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models

Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware. The YOLO family has become widely deployed for this reason, yet most YOLO detectors still rely on non-maximum suppression at inference, carry heavy detection heads due to Distribution Focal Loss, require long training schedules, and can leave the smallest objects without positive label assignments. We present Ultralytics YOLO26, a unified real-time vision model family that addresses these limitations through coordinated architecture and training advances. YOLO26 uses a dual-head design for native NMS-free end-to-end inference and removes DFL entirely, yielding a lighter head with unconstrained regression range. Its training pipeline combines MuSGD, a hybrid Muon-SGD optimizer adapted from large language model training; Progressive Loss, which shifts supervision toward the inference-time head; and STAL, a label assignment strategy that guarantees positive coverage for small objects. Beyond detection, YOLO26 introduces task-specific head and loss designs for instance segmentation, pose estimation, and oriented detection, producing consistent gains across tasks and scales. The family spans five scales (n/s/m/l/x) and supports detection, instance segmentation, pose estimation, classification, and oriented detection in a single pipeline, with an open-vocabulary extension, YOLOE-26, for text-, visual-, and prompt-free inference. Across all scales, YOLO26 achieves 40.9-57.5 mAP on COCO at 1.7-11.8 ms T4 TensorRT latency, advancing the accuracy-latency Pareto front over prior real-time detectors, while YOLOE-26x reaches 40.6 AP on LVIS minival under text prompting. Code and models are available at https://github.com/ultralytics/ultralytics.

Glenn Jocher, Jing Qiu, Mengyu Liu +3

Ultralytics

Open-Vocabulary Object DetectionInstance SegmentationEfficient Object DetectionObject DetectionReal-Time Object Detection
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Jun 1, 2026

Tiny Collaborative Inference for Occlusion-Robust Object Detection

Edge AI nodes for search and rescue are increasingly expected to run computer vision locally, yet ultra-low-end hardware imposes hard constraints on memory, compute, and inter-device communication. This work addresses occlusion-robust object detection on devices with less than 1 MB SRAM by combining an MCUNet backbone, a YOLOv2 detection head, and Lite quantisation. Two collaborative inference strategies are evaluated: feature-level fusion, concatenating intermediate feature maps, and decision-level fusion via Weighted Boxes Fusion (WBF). WBF outperforms feature-level fusion under all tested occlusion conditions, yielding gains of up to +0.2736 mAP in asymmetric scenarios. Extending fusion to three views improves accuracy further (up to +0.3827 mAP) at modest communication overhead (~1.3 KB per exchange). Hardware experiments progress from a host-assisted USB-relay baseline to a Wi-Fi peer-to-peer deployment on two Coral Dev Board Micro units, where WBF executes on-device with negligible communication energy relative to inference. In a 301.9 s autonomous session of 108 frames, fused output is produced on 61 frames versus 47 for a single board - a coverage gain of +29.8%. A decentralised federated learning feasibility note is included but not treated as a primary result, as performance remains limited under non-iid data. The results support decision-level fusion as a viable option for improving occlusion robustness in small-scale edge object detection, including host-free multi-board operation on ultra-low-end hardware.

Chieh-Tung Cheng, Mustafa Aslanov, Eiman Kanjo

Imperial College London, United Kingdom · Nottingham Trent University, United Kingdom

Edge InferenceEfficient Object DetectionObject Detection
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May 27, 2026

Lightweight SAR Ship Detection via Contrastive Distillation

Deep convolutional and transformer-based detectors achieve strong performance for SAR ship detection but are often computationally prohibitive for real-time or onboard deployment. Lightweight models offer improved efficiency yet struggle to capture the complex structural relationships inherent in SAR backscatter. Most existing SAR knowledge-distillation approaches rely on feature or logit matching, which enforces localized activation similarity while neglecting the geometric relationships among object representations. We propose a Structured Unified Relational knowledGE distillation framework for SAR Ship detection (SURGE) that transfers relational geometry from a powerful teacher detector to a compact student detector using a contrastive InfoNCE objective in a shared projection embedding space. To the best of our knowledge, this work presents the first transformer-based SAR ship detector knowledge distillation framework in SAR domain. The framework is architecture-agnostic in the sense that it provides a common region-level distillation interface for two-stage, one-stage and transformer-based detectors without modifying their deployed architectures. Experiments on the SSDD and HRSID benchmarks demonstrate that the proposed method yields substantial improvements for two-stage detectors, achieving up to 6.2 mAP and 8.0 AP75 gains over baseline student and even surpassing teacher performance

Surendar Devasundaram, Banafsheh Saber Latibari, Abhijit Mahalanobis

University of Arizona · Department of Electrical and Computer Engineering · Tucson, Arizona, USA

Efficient Object DetectionRelational Knowledge DistillationObject DetectionSynthetic Aperture RadarKnowledge DistillationShip Detection
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May 24, 2026

TinyFormer: Preserving Tiny Objects in YOLO-DETR Hybrid Real-time Detectors

YOLO-series and DETR-based detectors struggle with tiny-object detection. YOLO-style models benefit from efficient dense prediction, but their large-stride backbones may suppress tiny instances in deep feature maps and make grid assignment ambiguous. DETR-based models remove hand-crafted post-processing through set prediction, yet they reason over coarse token grids, where tiny objects occupy only a few weak tokens and are easily overlooked during matching. To address these limitations, we propose TinyFormer, a unified YOLO--DETR hybrid real-time detector that combines ViT representations, NMS-free set prediction, and a YOLO-style pyramid neck for accurate small-object detection. TinyFormer introduces a Parallel Bi-fusion Module (PBM), which builds high-resolution shortcuts from shallow stages to the feature pyramid, preserving fine spatial details during multi-scale fusion. We further design a Spatial Semantic Adapter (SSA) to compensate for the spatial loss caused by coarse tokenization. SSA extracts high-resolution cues from early stages and injects them into transformer token embeddings, improving tiny-object localization without sacrificing the global modeling ability of DETR. Experiments on MS COCO show that TinyFormer consistently outperforms recent YOLO-series detectors and the strong DEIMv2 baseline. TinyFormer-X achieves 58.4% AP even without PBM, while adding PBM improves the overall AP to 58.5% and brings a 1.6% AP gain on small objects. With Objects365 pre-training, TinyFormer-X-PBM reaches 60.2% AP, surpassing RF-DETR and other Objects365-pretrained detectors with fewer parameters and lower computation. These results demonstrate that TinyFormer bridges dense YOLO-style feature fusion and DETR-style set prediction, providing a strong accuracy-efficiency trade-off for real-time tiny-object detection. Code is available at https://github.com/mmpmmpmmpjosh/TinyFormer.

Jun-Wei Hsieh, Meng-Yu Kao, Ghufron Wahyu Kurniawan +1

College of Artificial Intelligence, National Yang Ming Chiao Tung University, Taiwan · 2Mitsubishi Electric Research Laboratories

Hybrid CNN-Transformer ArchitecturesSmall Object DetectionEfficient Object DetectionDetection TransformerReal-Time Object Detection
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May 14, 2026

MR2-ByteTrack: CNN and Transformer-based Video Object Detection for AI-augmented Embedded Vision Sensor Nodes

Modern smart vision sensors need on-device intelligence to process video streams, as cloud computing is often impractical due to bandwidth, latency, and privacy constraints. However, these sensory systems typically rely on ultra-low-power microcontrollers (MCUs) with limited memory and compute, making conventional video object detection methods, which require feature storage or multi-frame buffering, unfeasible. To address this challenge, we introduce Multi-Resolution Rescored ByteTrack (MR2-ByteTrack), a Video Object Detection (VOD) method tailored for MCU-based embedded vision nodes. MR2-ByteTrack reduces computational cost by alternating between full- and low-resolution inference, while linking detections across frames via ByteTrack and correcting misclassifications through the Rescore algorithm, which applies probability union rules to aggregate detection confidence scores across frames. We apply our approach to both a CNN-based detector and a Transformer-based model, demonstrating its generality across architectures with fundamentally different spatial processing. Experiments on ImageNetVID demonstrate that MR2-ByteTrack maintains accuracy, achieving mAP scores of up to 49.0 for the CNN-based models and 48.7 for the Transformer, while reducing multiply-accumulate operations by as much as 53% for the CNNs and 32% for the Transformer. When deployed on GAP9, an ultra-low-power RISC-V multicore MCU, our method yields up to 55% energy savings compared to processing only full-resolution images, enabling the first real-time Transformer-based VOD on an MCU-class embedded vision node. Code available at https://github.com/Bomps4/Multi_Resolution_Rescored_ByteTrack/tree/IEEE_Access

Luca Bompani, Manuele Rusci, Luca Benini +2

Electrical, Electronic and Information Engineering (DEI), University of Bologna, Italy. · Department of Electrical Engineering (ESAT), KU Leuven, Belgium. · Integrated Systems Laboratory (IIS), ETH Zürich, Switzerland. +1

Edge InferenceEfficient Object DetectionVideo Object DetectionReal-Time Object Detection
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May 13, 2026

Pattern-Enhanced RT-DETR for Multi-Class Battery Detection

Accurate and efficient battery detection is increasingly important for applications in electronic waste recycling, industrial quality control, and automated sorting systems. In this paper, we present both a comprehensive benchmark and a novel method for multi-class battery detection. We systematically compare three CNN-based detectors (YOLOv8n, YOLOv8s, YOLO11n) and two transformer-based detectors (RT-DETR-L, RT-DETR-X) on a publicly available dataset of approximately 8,591 annotated images under identical experimental conditions, and further propose PaQ-RT-DETR, which introduces pattern-based dynamic query generation into RT-DETR to alleviate query activation imbalance with negligible computational overhead. Among baselines, YOLO11n achieves the best CNN-based accuracy (mAP@50: 0.779) at only 2.6M parameters, while YOLOv8n delivers the fastest inference at ~1,667 FPS. PaQ-RT-DETR-X achieves the highest overall mAP@50 of 0.782, surpassing RT-DETR-X by +2.8% with consistent per-class gains across all six battery categories including the data-scarce Bike Battery class. Our findings provide practical guidance for selecting object detection models in battery-related industrial applications.

Xu Zhong, Enyuan Hu

Independent Researcher · Chemistry Division Brookhaven National Laboratory NY, USA

Efficient Object DetectionDetection TransformerObject DetectionReal-Time Object Detection
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May 10, 2026

AnyDepth-DETR/-YOLO: Any-depth object detection with a single network

Modern object detectors are static, fixed-depth networks optimized for a single operating point, requiring separate models for different deployment scenarios. We present an any-depth detection framework that enables a single network to span a continuous range of accuracy--efficiency trade-offs by controlling depth at inference time without retraining. Each backbone and neck stage is divided into an essential path, which always executes, and a skippable refinement path; this decomposition preserves the full multi-scale feature hierarchy at every depth configuration, unlike conventional early exiting that discards entire stages. To train such a network, jointly optimizing many sub-networks of varying depth introduces conflicting gradient signals. We address this via self-distillation between only the two extremes, with prediction-level and feature-level alignment losses that enforce stage-wise modularity, ensuring the outputs of each stage remain compatible regardless of the paths taken. Instantiated on RT-DETR and YOLOv12, our full-depth configurations match or surpass their respective SOTA baselines with negligible parameter overhead, while the most efficient configurations achieve up to 1.82×1.82\times speedup at a cost of only 2.0 AP, all from a single set of weights.

Woochul Kang, Hyungseop Lee, Jiho Lee

Incheon Nat’l Univ.

Efficient Neural Network InferenceDynamic Neural NetworksEfficient Object DetectionObject DetectionReal-Time Object Detection
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May 7, 2026

XiYOLO: Energy-Aware Object Detection via Iterative Architecture Search and Scaling

Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints while still providing reliable perception for downstream autonomy. Existing energy-aware NAS methods often target limited deployment settings, while real energy remains difficult to optimize because it is highly device-dependent and costly to measure. We address these challenges with an energy-adaptive framework that combines an energy-aware XiResOFA search space, a two-stage energy estimator, and iterative search to identify a single energy-efficient base architecture. We then apply compound scaling to transform this base design into the XiYOLO family across deployment budgets, enabling interpretable accuracy-energy tradeoffs under sparse hardware measurements. Experiments on PascalVOC, COCO, and real-device deployment show that XiYOLO achieves a stronger energy-accuracy tradeoff than YOLO baselines. On PascalVOC, the medium XiYOLO model reaches 86.15 mAP50 while reducing energy relative to YOLOv12m by 20.6% on GPU and 35.9% on NPU. On COCO, XiYOLO reduces energy relative to YOLOv12 by up to 53.7% on GPU and 51.6% on NPU at the small scale. The proposed two-stage estimator also improves sample efficiency over a joint predictor under few-shot adaptation with only 2-20 target-device samples.

Tony Tran, Richie R. Suganda, Bin Hu

Department of Research Computing University of Houston Houston, TX 77479 · Department of Electrical and Computer Engineering University of Houston Houston, TX 77479 · Department of Engineering Technology University of Houston Houston, TX 77479

Energy-Efficient MLEdge InferenceEfficient Object DetectionHardware-Aware NASObject DetectionNeural Architecture Search
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Apr 16, 2026

Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection

Transformer-based detectors have advanced small-object detection, but they often remain inefficient and vulnerable to background-induced query noise, which motivates deep decoders to refine low-quality queries. We present HELP (Heatmap-guided Embedding Learning Paradigm), a noise-aware positional-semantic fusion framework that studies where to embed positional information by selectively preserving positional encodings in foreground-salient regions while suppressing background clutter. Within HELP, we introduce Heatmap-guided Positional Embedding (HPE) as the core embedding mechanism and visualize it with a heatbar for interpretable diagnosis and fine-tuning. HPE is integrated into both the encoder and decoder: it guides noise-suppressed feature encoding by injecting heatmap-aware positional encoding, and it enables high-quality query retrieval by filtering background-dominant embeddings via a gradient-based mask filter before decoding. To address feature sparsity in complex small targets, we integrate Linear-Snake Convolution to enrich retrieval-relevant representations. The gradient-based heatmap supervision is used during training only, incurring no additional gradient computation at inference. As a result, our design reduces decoder layers from eight to three and achieves a 59.4% parameter reduction (66.3M vs. 163M) while maintaining consistent accuracy gains under a reduced compute budget across benchmarks. Code Repository: https://github.com/yidimopozhibai/Noise-Suppressed-Query-Retrieval

Yangchen Zeng, Zhenyu Yu, Dongming Jiang +5

Southeast University · Fudan University · The University of Texas at Dallas +6

Small Object DetectionEfficient Object DetectionDetection TransformerEfficient ViTsPositional Encoding
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Apr 14, 2026

DroneScan-YOLO: Redundancy-Aware Lightweight Detection for Tiny Objects in UAV Imagery

Aerial object detection in UAV imagery presents unique challenges due to the high prevalence of tiny objects, adverse environmental conditions, and strict computational constraints. Standard YOLO-based detectors fail to address these jointly: their minimum detection stride of 8 pixels renders sub-32px objects nearly undetectable, their CIoU loss produces zero gradients for non-overlapping tiny boxes, and their architectures contain significant filter redundancy. We propose DroneScan-YOLO, a holistic system contribution that addresses these limitations through four coordinated design choices: (1) increased input resolution of 1280x1280 to maximize spatial detail for tiny objects, (2) RPA-Block, a dynamic filter pruning mechanism based on lazy cosine-similarity updates with a 10-epoch warm-up period, (3) MSFD, a lightweight P2 detection branch at stride 4 adding only 114,592 parameters (+1.1%), and (4) SAL-NWD, a hybrid loss combining Normalized Wasserstein Distance with size-adaptive CIoU weighting, integrated into YOLOv8's TaskAligned assignment pipeline. Evaluated on VisDrone2019-DET, DroneScan-YOLO achieves 55.3% mAP@50 and 35.6% mAP@50-95, outperforming the YOLOv8s baseline by +16.6 and +12.3 points respectively, improving recall from 0.374 to 0.518, and maintaining 96.7 FPS inference speed with only +4.1% parameters. Gains are most pronounced on tiny object classes: bicycle AP@50 improves from 0.114 to 0.328 (+187%), and awning-tricycle from 0.156 to 0.237 (+52%).

Yann V. Bellec

Small Object DetectionAerial Object DetectionEfficient Object DetectionStructured Pruning
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Apr 6, 2026

RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation

This paper presents RAVEN, a computationally efficient deep learning architecture for FMCW radar perception. The method processes raw ADC data in a chirp-wise streaming manner, preserves MIMO structure through independent receiver state-space encoders, and uses a learnable cross-antenna mixing module to recover compact virtual-array features. It also introduces an early-exit mechanism so the model can make decisions using only a subset of chirps when the latent state has stabilized. Across automotive radar benchmarks, the approach reports strong object detection and BEV free-space segmentation performance while substantially reducing computation and end-to-end latency compared with conventional frame-based radar pipelines.

Anuvab Sen, Mir Sayeed Mohammad, Saibal Mukhopadhyay

Georgia Institute of Technology, Atlanta, Georgia, USA

mmWave Radar SensingEfficient Object DetectionObject Detection
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Apr 4, 2026

ComPrivDet: Efficient Privacy Object Detection in Compressed Domains Through Inference Reuse

As the Internet of Things (IoT) becomes deeply embedded in daily life, users are increasingly concerned about privacy leakage, especially from video data. Since frame-by-frame protection in large-scale video analytics (e.g., smart communities) introduces significant latency, a more efficient solution is to selectively protect frames containing privacy objects (e.g., faces). Existing object detectors require fully decoded videos or per-frame processing in compressed videos, leading to decoding overhead or reduced accuracy. Therefore, we propose ComPrivDet, an efficient method for detecting privacy objects in compressed video by reusing I-frame inference results. By identifying the presence of new objects through compressed-domain cues, ComPrivDet either skips P- and B-frame detections or efficiently refines them with a lightweight detector. ComPrivDet maintains 99.75% accuracy in private face detection and 96.83% in private license plate detection while skipping over 80% of inferences. It averages 9.84% higher accuracy with 75.95% lower latency than existing compressed-domain detection methods.

Yunhao Yao, Zhiqiang Wang, Ruiqi Li +3

University of Science and Technology of China (USTC)

Object DetectionVideo Object DetectionEfficient Object Detection
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Apr 3, 2026

SFFNet: Synergistic Feature Fusion Network With Dual-Domain Edge Enhancement for UAV Image Object Detection

Object detection in unmanned aerial vehicle (UAV) images remains a highly challenging task, primarily caused by the complexity of background noise and the imbalance of target scales. Traditional methods easily struggle to effectively separate objects from intricate backgrounds and fail to fully leverage the rich multi-scale information contained within images. To address these issues, we have developed a synergistic feature fusion network (SFFNet) with dual-domain edge enhancement specifically tailored for object detection in UAV images. Firstly, the multi-scale dynamic dual-domain coupling (MDDC) module is designed. This component introduces a dual-driven edge extraction architecture that operates in both the frequency and spatial domains, enabling effective decoupling of multi-scale object edges from background noise. Secondly, to further enhance the representation capability of the model's neck in terms of both geometric and semantic information, a synergistic feature pyramid network (SFPN) is proposed. SFPN leverages linear deformable convolutions to adaptively capture irregular object shapes and establishes long-range contextual associations around targets through the designed wide-area perception module (WPM). Moreover, to adapt to the various applications or resource-constrained scenarios, six detectors of different scales (N/S/M/B/L/X) are designed. Experiments on two challenging aerial datasets (VisDrone and UAVDT) demonstrate the outstanding performance of SFFNet-X, achieving 36.8 AP and 20.6 AP, respectively. The lightweight models (N/S) also maintain a balance between detection accuracy and parameter efficiency. The code will be available at https://github.com/CQNU-ZhangLab/SFFNet.

Wenfeng Zhang, Jun Ni, Yue Meng +4

College of Computer and Information Science, Chongqing Normal University, Chongqing 401333, China · CETC Yizhihang (Chongqing) Technology Co., Ltd, Chongqing 400031, China · School of Computer Engineering, Weifang University, Weifang 261061, China +1

Object DetectionAerial Object DetectionEfficient Object DetectionMulti-Scale Feature Fusion
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Apr 3, 2026

DeCo-DETR: Decoupled Cognition DETR for efficient Open-Vocabulary Object Detection

Open-vocabulary object detection (OVOD) enables models to recognize objects beyond predefined categories, but existing approaches remain limited in practical deployment. On the one hand, multimodal designs often incur substantial computational overhead due to their reliance on text encoders at inference time. On the other hand, tightly coupled training objectives introduce a trade-off between closed-set detection accuracy and open-world generalization. Thus, we propose Decoupled Cognition DETR (DeCo-DETR), a vision-centric framework that addresses these challenges through a unified decoupling paradigm. Instead of depending on online text encoding, DeCo-DETR constructs a hierarchical semantic prototype space from region-level descriptions generated by pre-trained LVLMs and aligned via CLIP, enabling efficient and reusable semantic representation. Building upon this representation, the framework further disentangles semantic reasoning from localization through a decoupled training strategy, which separates alignment and detection into parallel optimization streams. Extensive experiments on standard OVOD benchmarks demonstrate that DeCo-DETR achieves competitive zero-shot detection performance while significantly improving inference efficiency. These results highlight the effectiveness of decoupling semantic cognition from detection, offering a practical direction for scalable OVOD systems.

Siheng Wang, Yanshu Li, Bohan Hu +12

Jiangsu University · MBZUAI · Brown University +5

Open-Vocabulary Object DetectionDetection TransformerEfficient Object Detection
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Mar 30, 2026

BlankSkip: Early-exit Object Detection onboard Nano-drones

Deploying tiny computer vision Deep Neural Networks (DNNs) on-board nano-sized drones is key for achieving autonomy, but is complicated by the extremely tight constraints of their computational platforms (approximately 10 MiB memory, 1 W power budget). Early-exit adaptive DNNs that dial down the computational effort for "easy-to-process" input frames represent a promising way to reduce the average inference latency. However, while this approach is extensively studied for classification, its application to dense tasks like object detection (OD) is not straightforward. In this paper, we propose BlankSkip, an adaptive network for on-device OD that leverages a simple auxiliary classification task for early exit, i.e., identifying frames with no objects of interest. With experiments using a real-world nano-drone platform, the Bitcraze Crazyflie 2.1, we achieve up to 24% average throughput improvement with a limited 0.015 mean Average Precision (mAP) drop compared to a static MobileNet-SSD detector, on a state-of-the-art nano-drones OD dataset.

Carlo Marra, Beatrice Alessandra Motetti, Alessio Burrello +3

†Politecnico di Torino, Turin, Italy

Efficient Object DetectionObject DetectionEdge InferenceAerial Object Detection
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Mar 27, 2026

Learnable Instance Attention Filtering for Adaptive Detector Distillation

As deep vision models grow increasingly complex to achieve higher performance, deployment efficiency has become a critical concern. Knowledge distillation (KD) mitigates this issue by transferring knowledge from large teacher models to compact student models. While many feature-based KD methods rely on spatial filtering to guide distillation, they typically treat all object instances uniformly, ignoring instance-level variability. Moreover, existing attention filtering mechanisms are typically heuristic or teacher-driven, rather than learned with the student. To address these limitations, we propose Learnable Instance Attention Filtering for Adaptive Detector Distillation (LIAF-KD), a novel framework that introduces learnable instance selectors to dynamically evaluate and reweight instance importance during distillation. Notably, the student contributes to this process based on its evolving learning state. Experiments on the KITTI and COCO datasets demonstrate consistent improvements, with a 2% gain on a GFL ResNet-50 student without added complexity, outperforming state-of-the-art methods.

Chen Liu, Qizhen Lan, Zhicheng Ding +2

Dept. of CS, University of Alabama at Birmingham · Dept. of CS, Bowling Green State University

Knowledge DistillationFeature-Based Knowledge DistillationSelective Knowledge DistillationEfficient Object Detection
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Mar 26, 2026

SDD-YOLO: A Small-Target Detection Framework for Ground-to-Air Anti-UAV Surveillance with Edge-Efficient Deployment

Detecting small unmanned aerial vehicles (UAVs) from a ground-to-air (G2A) perspective presents significant challenges, including extremely low pixel occupancy, cluttered aerial backgrounds, and strict real-time constraints. Existing YOLO-based detectors are primarily optimized for general object detection and often lack adequate feature resolution for sub-pixel targets, while introducing complexities during deployment. In this paper, we propose SDD-YOLO, a small-target detection framework tailored for G2A anti-UAV surveillance. To capture fine-grained spatial details critical for micro-targets, SDD-YOLO introduces a P2 high-resolution detection head operating at 4 times downsampling. Furthermore, we integrate the recent architectural advancements from YOLO26, including a DFL-free, NMS-free architecture for streamlined inference, and the MuSGD hybrid training strategy with ProgLoss and STAL, which substantially mitigates gradient oscillation on sparse small-target signals. To support our evaluation, we construct DroneSOD-30K, a large-scale G2A dataset comprising approximately 30,000 annotated images covering diverse meteorological conditions. Experiments demonstrate that SDD-YOLO-n achieves a [email protected] of 86.0% on DroneSOD-30K, surpassing the YOLOv5n baseline by 7.8 percentage points. Extensive inference analysis shows our model attains 226 FPS on an NVIDIA RTX 5090 and 35 FPS on an Intel Xeon CPU, demonstrating exceptional efficiency for future edge deployment.

Pengyu Chen, Haotian Sa, Yiwei Hu +2

Chien-Shiung Wu College, Southeast University, Nanjing, China · School of Information Science and Engineering, Southeast University, Nanjing, China

Small Object DetectionAerial Object DetectionReal-Time Object DetectionEfficient Object Detection
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Mar 24, 2026

TorR: Towards Brain-Inspired Task-Oriented Reasoning via Cache-Oriented Algorithm-Architecture Co-design

Task-oriented object detection (TOOD) atop CLIP offers open-vocabulary, prompt-driven semantics, yet dense per-window computation and heavy memory traffic hinder real-time, power-limited edge deployment. We present TorR, a brain-inspired algorithm--architecture co-design that replaces CLIP-style dense alignment with a hyperdimensional (HDC) associative reasoner and turns temporal coherence into reuse. On the algorithm side, TorR reformulates alignment as HDC similarity and graph composition, introducing partial-similarity reuse via (i) query caching with per-class score accumulation, (ii) exact δδ-updates when only a small set of hypervector bits change, and (iii) similarity/load-gated bypass under high system load. On the architecture side, TorR instantiates a lane-scalable, bit-sliced item memory with bank/precision gating and a lightweight controller that schedules bypass/δδ/full paths to meet RT-30/RT-60 targets as object counts vary. Synthesized in a TSMC 28,nm process and exercised with a cycle-accurate simulator, TorR sustains real-time throughput with millijoule-scale energy per window (≈\approx50,mJ at 60,FPS; ≈\approx113,mJ at 30,FPS) and low latency jitter, while delivering competitive [email protected] across five task prompts (mean 44.27%) within a bounded margin to strong VLM baselines, but at orders-of-magnitude lower energy. The design exposes deployment-time configurability (effective dimension D′D', thresholds, precision) to trade accuracy, latency, and energy for edge budgets.

Hyunwoo Oh, SungHeon Jeong, Suyeon Jang +4

Department of Computer Science, University of California, Irvine Irvine, CA, USA

Efficient Object DetectionHardware-Software Co-DesignReal-Time Object DetectionEdge InferenceObject DetectionHyperdimensional Computing
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