Object Detection

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Object detection asks models to identify what objects are present and where they appear, usually by predicting labeled bounding boxes in images or video. Common approaches include fast one-stage CNN detectors such as YOLO, proposal-based Faster R-CNN, and end-to-end transformer detectors such as DETR; feature pyramids and attention help handle objects at different scales. Work also explores oriented and 3D detection, sensor fusion, open-vocabulary recognition, and learning with limited or changing labels, with COCO, VisDrone, DOTA, KITTI, and nuScenes among the benchmarks used. A central challenge is transferring strong benchmark results to small, occluded, or unfamiliar objects and real-world conditions while keeping models efficient and evaluations reliable despite incomplete annotations.

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624 papers51 papers in the last four weeks, down 25% on the four weeks before. 0.3% of all new papers.

LFA: Layer Feature Attention for Run-Time Introspection of 2D Object Detectors in Automated Driving
May 29, 2026Mert Keser, Alois KnollAutonomous Driving Perception Object Detection
Aumovio SE, Germany† · Technical University of Munich, Germany†‡
Detect in Any Scene: An Agentic Framework for Object Detection with Experience-Aware Reasoning
May 29, 2026Wenlun Zhang, Jun Yin, Kentaro YoshiokaObject Detection
Keio University · Tsinghua University
AI for Maritime Security: Comparative Evaluation of CNN and Vision Transformer Architectures for Maritime Object Detection
May 28, 2026Ismet Gocer, Zakirul Bhuiayn, Shakeel Ahmad +1Vision Transformer Object Detection
Southampton Solent University School of Technology and Maritime Industries Southampton, United Kingdom
GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection
May 28, 2026Jiacong Liu, Shu Luo, Yikai Qin +3Synthetic Data Augmentation Few-Shot Domain Adaptation
Huazhong University of Science and Technology
Lightweight SAR Ship Detection via Contrastive Distillation
May 27, 2026Surendar Devasundaram, Banafsheh Saber Latibari, Abhijit MahalanobisEfficient Object Detection Relational Knowledge Distillation
University of Arizona · Department of Electrical and Computer Engineering · Tucson, Arizona, USA
LV-OSD: Language-Vision-Complementary Open-Set Object Detection
May 27, 2026Yupeng Zhang, Ruize Han, Wei Feng +2Open-Vocabulary Object Detection Object Detection
College of Intelligence and Computing, Tianjin University, Tianjin 300350, China · Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen 518107, Guangdong, China
Intra-YOLO: A Small Object Detection Model for Caries and Molar-Incisor Hypomineralization in Intraoral Photography Based on Transfer Learning with Reinforcement Learning
May 27, 2026Po-Lun Chwang, Po-Yu Chang, Wen-Liang Lin +3Medical Image Analysis Small Object Detection
Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan · Taipei Medical University Hospital, Taipei, Taiwan · Wan Fang Hospital, Taipei Medical University, Taiwan
LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding
May 26, 2026Shihao Wang, Shilong Liu, Yuanguo Kuang +10Efficient VLM Inference Vision-Language Grounding
The Hong Kong Polytechnic University · NVIDIA · Princeton University +2
Semantic Robustness Probing via Inpainting: An Interactive Tool for Safety-Critical Object Detection
May 26, 2026Nico Steckhan, Krutarth Prajapati, Weija Shao +1Object Detection AI Safety
Federal Institute for Occupational Safety and Health (BAuA), Germany
YOLO26-RipeLoc Lite: A lightweight architecture for tomato ripeness detection and picking point localization in greenhouse robotic harvesting
May 26, 2026Rajmeet Singh, Manveen Kaur, Shahpour Alirezaee +1Agricultural Image Analysis Robotic Grasping
Department of Mechanical Engineering Khalifa University Abu Dhabi, UAE · Department of Mechanical Engineering University of Windsor Windsor, Canada
Small Object Detection in Industrial Recycling: A New Dataset and YOLO Performance Evaluation
May 26, 2026Oussama Messai, Abbass Zein-Eddine, Abdelouahid Bentamou +4Small Object Detection Object Detection
Mines Saint-Etienne, CNRS, UMR 5307 LGF, F-42023 Saint-Etienne, France · Siléane Group, 12 Rue Louis Soulier, 42000 Saint-Etienne, France · Orano Group, Orano Recyclage La Hague – 50444 La Hague Cedex, France
LRDDv3: High-Resolution Long-Range Drone Detection Dataset with Range Information and Thermal Data
May 25, 2026Knut Peterson, Zaid Mayers, Azmain Yousuf +5Object Detection Aerial Object Detection
iMaPLe Research Lab, Drexel University · University of Maine
SAM3-Assisted Training of Lightweight YOLO Models for Precision Pig Farming
May 25, 2026Marcos Vinicius Mendes Faria, Thiago Borges Pereira, Isabella C. F. S. Condotta +2Edge Inference Pseudo-Labeling
Coordenadoria de Inform´atica, Instituto Federal do Esp´ırito Santo (IFES), Serra, ES · Department of Animal Sciences, University of Illinois at Urbana-Champaign, USA
BED-SAM2: Boundary-Enhanced-Depth SAM2 via Monocular Geometric Priors
May 24, 2026Tyler Rust, Dara McNally, Kyle O'Donnell +2Image Segmentation Salient Object Detection
University of Delaware · University of South Florida · DEVCOM Army Research Laboratory
YOLO26 vs. YOLOv8: A Comprehensive Architectural Benchmark of Next-Generation Real-Time Object Detection Models
May 24, 2026Chidera G. Oguine, Kanyifeechukwu J. Oguine, Obiozor M. Oguine +1Edge Inference Object Detection
University of Abuja, Nigeria · Vanderbilt University, USA · University of Notre Dame, USA
From Full Boards to Tiny Defects: Scale-Aware Tile Inference with Topology-Aware Merging for High-Resolution PCB Defect Detection
May 23, 2026Mohammad Alijanpour Shalmani, Alale Rezvani Boroujeni, Ali Amini +1Small Object Detection Industrial Inspection
Dept. of Electrical and Computer Engineering, 4000 Central Florida Blvd., Orlando, FL 32816 · Dept. of Marketing, 4000 Central Florida Blvd., Orlando, FL 32816 · Centre of Real Time Computer Systems, Faculty of Informatics, Kaunas University of Technology, K. Donelaičio g. 73, Kaunas, Lithuania, 44249
Calibrating Probabilistic Object Detectors with Annotator Disagreement
May 23, 2026Zhi Qin Tan, Owen Addison, Yunpeng LiAnnotator Disagreement Object Detection
Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, United Kingdom
AdaFuse-Det: Adaptive Cross-Modal Fusion of Event Cameras for Robust Object Detection in Low-Light RGB Imagery
May 23, 2026Raju Imandi, Chethana B, Bharatesh Chakravarthi +3Event-Based Object Detection Object Detection
SRM University AP, India · Aptiv, Bengaluru, India · Arizona State University, USA +2
Lake Detection and Water Quality Estimation in Sentinel-2 Data
May 23, 2026Iulia Pleşu, Alexandra Băicoianu, Ioana Cristina PlajerScientific Visualization Remote Sensing
Transilvania University of Braşov, Faculty of Mathematics and Computer Science, Romania
FDDet: Achieving Data-Efficient Food Defect Detection Under Real-World Scenarios
May 23, 2026Ruihao Xu, Yong Liu, Yansong TangObject Detection Semi-Supervised Learning
Tsinghua Shenzhen International Graduate School, Tsinghua University
ViViD-5K: Vineyard vision dataset for field-based berry detection and segmentation and grape cluster closure estimation
May 23, 2026Xiangzhi Tong, Chengrui Zhang, Mac Flaherty +5Agricultural Image Analysis Plant Phenotyping
School of Electrical and Computer Engineering, Cornell University, NY, USA. · Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK. · Horticulture Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA. +1
Hoeffding Concept Bottleneck Models with Applications to Overhead Images
May 22, 2026Clément Bénard, Manon Arfib, Christophe Labreuche +1Concept Bottleneck Models Explainable Image Classification
Thales cortAIx-Labs, Palaiseau, France · Université Paris-Saclay, CentraleSupélec, Gif-sur-Yvette, France
DualMem: Bypassing the Objectness Bottleneck for Calibrated Unknown-Stream Filtering in Open-World Object Detection
May 22, 2026Yingjun Xiao, Xi Chen, Gang Fang +1Open-Vocabulary Object Detection Open-Set Recognition
a · School of Artificial Intelligence, Guangzhou University, Guangzhou, China · b +3
MDS-DETR: DETR with Masked Duplicate Suppressor
May 22, 2026Chanho Lee, Seunghee Koh, Yunho Jeon +1Detection Transformer Object Detection
Samsung Research, Seoul 06765, South Korea · Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea · Department of Artificial Intelligence Software, Hanbat National University, Daejeon 34158, South Korea
DefocusTrackerAI -- A Generalized Framework for the Automatic Detection of Defocused Particle Images
May 22, 2026Gonçalo Coutinho, Ana S. Moita, António L. N. Moreira +1Object Detection
IN+ Center for Innovation, Technology and Policy Research, Instituto Superior T´ecnico, University of Lisbon, Lisbon, Portugal · CINAMIL - Military Academy Research Center, Militart Academy, Portugal · IN+ Center for Innovation, Technology and Policy Research, Instituto Superior T´ecnico, University of Lisbon, Lisbon,Portugal +1
Decoupling Ego-Motion from Target Dynamics via Dual-Interval Motion Cues for UAV Detection
May 21, 2026Liuyang Wang, Feitian ZhangVideo Object Detection Object Detection
Robotics and Control Laboratory, Department of Robotics, School of Advanced Manufacturing and Robotics, and the State Key Laboratory of Turbulence and Complex Systems, Peking University, Beijing, 100871, China · Great Bay University, Guangdong 523808, China
Making the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light
May 21, 2026Valeria Pais, Malena Mendilaharzu, Daniele Faccio +3Synthetic Data Augmentation Autonomous Driving Perception
University of Glasgow · Chapel Ln, Glasgow G11 6EW, UK · Dotphoton +2
Detection of Virus and Small Cell Patches in Foci Images Using Switchable Convolution and Feature Pyramid Networks
May 21, 2026Amrita Singh, Snehasis MukherjeeSmall Object Detection Object Detection
Impact of Atmospheric Turbulence and Pointing Error on Earth Observation
May 21, 2026Celia Sánchez-de-Miguel, Antonio M. Mercado-Martínez, Beatriz Soret +2Image Corruption Robustness Remote Sensing
TELMA, University of Malaga Malaga, Spain
SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detection
May 20, 2026Wanying Tan, Shuo Yan, Dazhi Huang +7Explainable Artificial Intelligence Small Object Detection
Shenzhen University, Shenzhen, China · The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou,2026 China +2

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LFA: Layer Feature Attention for Run-Time Introspection of 2D Object Detectors in Automated Driving

Mert Keser, Alois Knoll

Aumovio SE, Germany† · Technical University of Munich, Germany†‡

Autonomous Driving Perception Object Detection

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Reliable object detection is critical for automated driving, yet even state-of-the-art detectors inevitably make errors that can compromise safety. Introspection methods that predict detector failures enable safer deployment by triggering fallback mechanisms or alerting human operators. However, existing approaches rely solely on last-layer features or hand-crafted statistics, discarding valuable information from earlier layers that capture different levels of visual abstraction. We propose Layer Feature Attention (LFA), a lightweight introspection method that learns to aggregate features from multiple backbone layers through an attention mechanism. Our key insight is that detection errors manifest differently across feature hierarchies: low-level layers capture fine-grained details essential for detecting small or occluded objects, while high-level layers encode semantic information for scene understanding. LFA learns layer importance weights end-to-end, enabling both improved error prediction and interpretable analysis of which feature levels are most indicative of detector failures. Extensive experiments on KITTI and BDD100K demonstrate that LFA achieves state-of-the-art introspection performance, outperforming single-layer baselines across multiple detector architectures.