Boundary-Aware Segmentation

Ask about this topic

Overview

Boundary-aware segmentation aims to delineate objects and structures accurately, especially where boundaries are weak, thin, ambiguous, or clinically important. Common approaches add edge or distance-field prediction, boundary-weighted and topology-aware losses, frequency or geometric features, and targeted refinement guided by uncertainty; these methods are used in medical, remote-sensing, natural-scene, and 3D segmentation. Work also adapts foundation models with prompts, boundary-aware decoders, or distilled edge cues, and evaluates contours with measures such as Boundary F-score and Hausdorff distance alongside region-overlap metrics. A central challenge is improving boundary reliability without sacrificing region accuracy, generalization, or efficiency, particularly when annotations are uncertain or target domains differ.

Latest papers

CardsList

106 papers6 papers in the last four weeks, down 57% on the four weeks before. 0.0% of all new papers.

Mar 13, 2026

Spatio-Semantic Expert Routing Architecture with Mixture-of-Experts for Referring Image Segmentation

Referring image segmentation aims to produce a pixel-level mask for the image region described by a natural-language expression. Although pretrained vision-language models have improved semantic grounding, many existing methods still rely on uniform refinement strategies that do not fully match the diverse reasoning requirements of referring expressions. Because of this mismatch, predictions often contain fragmented regions, inaccurate boundaries, or even the wrong object, especially when pretrained backbones are frozen for computational efficiency. To address these limitations, we propose SERA, a Spatio-Semantic Expert Routing Architecture for referring image segmentation. SERA introduces lightweight, expression-aware expert refinement at two complementary stages within a vision-language framework. First, we design SERA-Adapter, which inserts an expression-conditioned adapter into selected backbone blocks to improve spatial coherence and boundary precision through expert-guided refinement and cross-modal attention. We then introduce SERA-Fusion, which strengthens intermediate visual representations by reshaping token features into spatial grids and applying geometry-preserving expert transformations before multimodal interaction. In addition, a lightweight routing mechanism adaptively weights expert contributions while remaining compatible with pretrained representations. To make this routing stable under frozen encoders, SERA uses a parameter-efficient tuning strategy that updates only normalization and bias terms, affecting less than 1% of the backbone parameters. Experiments on standard referring image segmentation benchmarks show that SERA consistently outperforms strong baselines, with especially clear gains on expressions that require accurate spatial localization and precise boundary delineation.

Alaa Dalaq, Muzammil Behzad

King Fahd University of Petroleum and Mineral, Dhahran, 31261, Saudi Arabia · SDAIA-KFUPM Joint Research Center for Artificial Intelligence, Dhahran, 31261, Saudi Arabia

Referring Image SegmentationBoundary-Aware SegmentationMixture of Experts
AskPDF
Mar 11, 2026

Phase-Interface Instance Segmentation as a Visual Sensor for Laboratory Process Monitoring

Reliable visual monitoring of chemical experiments remains challenging in transparent glassware, where weak phase boundaries and optical artifacts degrade conventional segmentation. We formulate laboratory phenomena as the time evolution of phase interfaces and introduce the Chemical Transparent Glasses dataset 2.0 (CTG 2.0), a vessel-aware benchmark with 3,668 images, 23 glassware categories, and five multiphase interface types for phase-interface instance segmentation. Building on YOLO11m-seg, we propose LGA-RCM-YOLO, which combines Local-Global Attention (LGA) for robust semantic representation and a Rectangular Self-Calibration Module (RCM) for boundary refinement of thin, elongated interfaces. On CTG 2.0, the proposed model achieves 84.4% [email protected] and 58.43% [email protected], improving over the YOLO11m baseline by 6.42 and 8.75 AP points, respectively, while maintaining near real-time inference (13.67 FPS, RTX 3060). An auxiliary color-attribute head further labels liquid instances as colored or colorless with 98.71% precision and 98.32% recall. Finally, we demonstrate continuous process monitoring in separatory-funnel phase separation and crystallization, showing that phase-interface instance segmentation can serve as a practical visual sensor for laboratory automation.

Mingyue Li, Xin Yang, Shilin Yan +8

School of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing 400054, China · School of Chemistry and Chemical Engineering, Southwest University, Chongqing 400715, China · Department of computer science, University of North Texas, Denton 76207, Texas, United States +2

Instance SegmentationBoundary-Aware Segmentation
AskPDF
Mar 9, 2026

Bi-CamoDiffusion: A Boundary-informed Diffusion Approach for Camouflaged Object Detection

Bi-CamoDiffusion is introduced, an evolution of the CamoDiffusion framework for camouflaged object detection. It integrates edge priors into early-stage embeddings via a parameter-free injection process, enhancing boundary sharpness and preventing structural ambiguity. An optimization objective that unifies spatial accuracy, structural constraints, and uncertainty supervision is also proposed, allowing the model to capture of both the object's global context and its intricate boundary transitions. Evaluations across the CAMO, COD10K, and NC4K datasets show that Bi-CamoDiffusion surpasses the baseline, delivering sharper delineation of thin structures and protrusions while also minimizing false positives. The model consistently outperforms existing state-of-the-art methods across all evaluated metrics, including SmS_m, FβwF_β^{w}, EmE_m, and MAEMAE, demonstrating a more precise object-background separation and sharper boundary recovery. Code available at: https://github.com/plsuarez/Bi-CamoDiffusion

Patricia L. Suarez, Leo Thomas Ramos, Angel D. Sappa

ESPOL Polytechnic University · Computer Vision Center · Universitat Aut`onoma de Barcelona

Camouflaged Object DetectionImage SegmentationBoundary-Aware SegmentationDiffusion Models
AskPDF
Mar 9, 2026

Structure and Progress Aware Diffusion for Medical Image Segmentation

Medical image segmentation is crucial for computer-aided diagnosis, which necessitates understanding both coarse morphological and semantic structures, as well as carving fine boundaries. The morphological and semantic structures in medical images are beneficial and stable clues for target understanding. While the fine boundaries of medical targets (like tumors and lesions) are usually ambiguous and noisy since lesion overlap, annotation uncertainty, and so on, making it not reliable to serve as early supervision. However, existing methods simultaneously learn coarse structures and fine boundaries throughout the training process. In this paper, we propose a structure and progress-aware diffusion (SPAD) for medical image segmentation, which consists of a semantic-concentrated diffusion (ScD) and a boundary-centralized diffusion (BcD) modulated by a progress-aware scheduler (PaS). Specifically, the semantic-concentrated diffusion introduces anchor-preserved target perturbation, which perturbs pixels within a medical target but preserves unaltered areas as semantic anchors, encouraging the model to infer noisy target areas from the surrounding semantic context. The boundary-centralized diffusion introduces progress-aware boundary noise, which blurs unreliable and ambiguous boundaries, thus compelling the model to focus on coarse but stable anatomical morphology and global semantics. Furthermore, the progress-aware scheduler gradually modulates noise intensity of the ScD and BcD forming a coarse-to-fine diffusion paradigm, which encourage focusing on coarse morphological and semantic structures during early target understanding stages and gradually shifting to fine target boundaries during later contour adjusting stages.

Siyuan Song, Guyue Hu, Chenglong Li +3

School of Artificial Intelligence, Anhui University, 230601, Hefei, China · State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology, Anhui University, Hefei, 230601, China · Anhui Provincial Key Laboratory of Security Artificial Intelligence, Anhui University, Hefei, 230601, China +2

Diffusion Models for Medical Image SegmentationMedical Image SegmentationBoundary-Aware Segmentation
AskPDF
Mar 4, 2026

Polyp Segmentation Using Wavelet-Based Cross-Band Integration for Enhanced Boundary Representation

Accurate polyp segmentation is essential for early colorectal cancer detection, yet achieving reliable boundary localization remains challenging due to low mucosal contrast, uneven illumination, and color similarity between polyps and surrounding tissue. Conventional methods relying solely on RGB information often struggle to delineate precise boundaries due to weak contrast and ambiguous structures between polyps and surrounding mucosa. To establish a quantitative foundation for this limitation, we analyzed polyp-background contrast in the wavelet domain, revealing that grayscale representations consistently preserve higher boundary contrast than RGB images across all frequency bands. This finding suggests that boundary cues are more distinctly represented in the grayscale domain than in the color domain. Motivated by this finding, we propose a segmentation model that integrates grayscale and RGB representations through complementary frequency-consistent interaction, enhancing boundary precision while preserving structural coherence. Extensive experiments on four benchmark datasets demonstrate that the proposed approach achieves superior boundary precision and robustness compared to conventional models.

Haesung Oh, Jaesung Lee

Department of Artificial Intelligence, Chung-Ang University, Republic of Korea

Image SegmentationPolyp SegmentationBoundary-Aware Segmentation
AskPDF
Mar 3, 2026

Gated Differential Linear Attention: A Linear-Time Decoder for High-Fidelity Medical Segmentation

Medical image segmentation requires models that preserve fine anatomical boundaries while remaining practical for clinical deployment. Transformers capture long-range dependencies but incur quadratic attention cost, whereas CNNs are efficient but less effective at global reasoning. Linear attention offers O(N)\mathcal{O}(N) scaling, but often produces diffuse feature aggregation that weakens boundary-sensitive prediction. We introduce a gated differential linear-attention mixer for medical image segmentation. Its global path, Gated Differential Linear Attention (GDLA), performs differential subtraction between two kernelized attention branches over complementary query/key subspaces to suppress redundant responses, and employs a data-dependent gate for token refinement. A parallel local token-mixing branch with depthwise convolution strengthens neighborhood interactions for better refinement, and the two branches are fused while preserving O(N)\mathcal{O}(N) complexity. When instantiated in a pretrained Pyramid Vision Transformer (PVT)-based encoder--decoder model, \name achieves state-of-the-art results on the evaluated 2D medical segmentation benchmarks spanning CT, MRI, ultrasound, and dermoscopy, with a favorable accuracy--efficiency trade-off over closely related baselines. The code is publicly available at https://github.com/xmindflow/gdla.

Hongbo Zheng, Afshin Bozorgpour, Dorit Merhof +1

University of Illinois Urbana-Champaign · University of Regensburg

Medical Image SegmentationLinear AttentionGated AttentionBoundary-Aware SegmentationHybrid CNN-Transformer Architectures
AskPDF
Mar 3, 2026

SEP-YOLO: Fourier-Domain Feature Representation for Transparent Object Instance Segmentation

Transparent object instance segmentation presents significant challenges in computer vision, due to the inherent properties of transparent objects, including boundary blur, low contrast, and high dependence on background context. Existing methods often fail as they depend on strong appearance cues and clear boundaries. To address these limitations, we propose SEP-YOLO, a novel framework that integrates a dual-domain collaborative mechanism for transparent object instance segmentation. Our method incorporates a Frequency Domain Detail Enhancement Module, which separates and enhances weak highfrequency boundary components via learnable complex weights. We further design a multi-scale spatial refinement stream, which consists of a Content-Aware Alignment Neck and a Multi-scale Gated Refinement Block, to ensure precise feature alignment and boundary localization in deep semantic features. We also provide high-quality instance-level annotations for the Trans10K dataset, filling the critical data gap in transparent object instance segmentation. Extensive experiments on the Trans10K and GVD datasets show that SEP-YOLO achieves state-of-the-art (SOTA) performance.

Fengming Zhang, Tao Yan, Jianchao Huang

Jiangnan University, Wuxi, China

Instance SegmentationBoundary-Aware Segmentation
AskPDF
Mar 2, 2026

MixerCSeg: An Efficient Mixer Architecture for Crack Segmentation via Decoupled Mamba Attention

Feature encoders play a key role in pixel-level crack segmentation by shaping the representation of fine textures and thin structures. Existing CNN-, Transformer-, and Mamba-based models each capture only part of the required spatial or structural information, leaving clear gaps in modeling complex crack patterns. To address this, we present MixerCSeg, a mixer architecture designed like a coordinated team of specialists, where CNN-like pathways focus on local textures, Transformer-style paths capture global dependencies, and Mamba-inspired flows model sequential context within a single encoder. At the core of MixerCSeg is the TransMixer, which explores Mamba's latent attention behavior while establishing dedicated pathways that naturally express both locality and global awareness. To further enhance structural fidelity, we introduce a spatial block processing strategy and a Direction-guided Edge Gated Convolution (DEGConv) that strengthens edge sensitivity under irregular crack geometries with minimal computational overhead. A Spatial Refinement Multi-Level Fusion (SRF) module is then employed to refine multi-scale details without increasing complexity. Extensive experiments on multiple crack segmentation benchmarks show that MixerCSeg achieves state-of-the-art performance with only 2.05 GFLOPs and 2.54 M parameters, demonstrating both efficiency and strong representational capability. The code is available at https://github.com/spiderforest/MixerCSeg.

Zilong Zhao, Zhengming Ding, Pei Niu +2

School of Qilu Transportation, Shandong University, China · Department of Computer Science, Tulane University, USA

Crack SegmentationHybrid CNN-Transformer ArchitecturesBoundary-Aware Segmentation
AskPDF
Feb 28, 2026

Explainable Continuous-Time Mask Refinement with Local Self-Similarity Priors for Medical Image Segmentation

Accurate semantic segmentation of foot ulcers is essential for automated wound monitoring, yet boundary delineation remains challenging due to tissue heterogeneity and poor contrast with surrounding skin. To overcome the limitations of standard intensity-based networks, we present LSS-LTCNet:an ante-hoc explainable framework synergizing deterministic structural priors with continuous-time neural dynamics. Our architecture departs from traditional black-box models by employing a Local Self-Similarity (LSS) mechanism that extracts dense, illumination-invariant texture descriptors to explicitly disentangle necrotic tissue from background artifacts. To enforce topological precision, we introduce a Liquid Time-Constant (LTC) refinement module that treats boundary evolution as an ODEgoverned dynamic system, iteratively refining masks over continuous time-steps. Comprehensive evaluation on the MICCAI FUSeg dataset demonstrates that LSS-LTCNet achieves state-of-the-art boundary alignment, securing a peak Dice score of 86.96% and an exceptional 95th percentile Hausdorff Distance (HD95) of 8.91 pixels. Requiring merely 25.70M parameters, the model significantly outperforms heavier U-Net and transformer baselines in efficiency. By providing inherent visual audit trails alongside high-fidelity predictions, LSS-LTCNet offers a robust and transparent solution for computer-aided diagnosis in mobile healthcare (mHealth) settings.

Rajdeep Chatterjee, Sudip Chakrabarty, Trishaani Acharjee

School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India · AmygdalaAI-India Lab, Bhubaneswar, India

Medical Image SegmentationBoundary-Aware SegmentationExplainable Medical Image Analysis
AskPDF
Feb 26, 2026

SGDC: Structurally-Guided Dynamic Convolution for Medical Image Segmentation

Spatially variant dynamic convolution provides a principled approach of integrating spatial adaptivity into deep neural networks. However, mainstream designs in medical segmentation commonly generate dynamic kernels through average pooling, which implicitly collapses high-frequency spatial details into a coarse, spatially-compressed representation, leading to over-smoothed predictions that degrade the fidelity of fine-grained clinical structures. To address this limitation, we propose a novel Structure-Guided Dynamic Convolution (SGDC) mechanism, which leverages an explicitly supervised structure-extraction branch to guide the generation of dynamic kernels and gating signals for structure-aware feature modulation. Specifically, the high-fidelity boundary information from this auxiliary branch is fused with semantic features to enable spatially-precise feature modulation. By replacing context aggregation with pixel-wise structural guidance, the proposed design effectively prevents the information loss introduced by average pooling. Experimental results show that SGDC achieves state-of-the-art performance on ISIC 2016, PH2, ISIC 2018, and CoNIC datasets, delivering superior boundary fidelity by reducing the Hausdorff Distance (HD95) by 2.05, and providing consistent IoU gains of 0.99%-1.49% over pooling-based baselines. Moreover, the mechanism exhibits strong potential for extension to other fine-grained, structure-sensitive vision tasks, such as small-object detection, offering a principled solution for preserving structural integrity in medical image analysis. To facilitate reproducibility and encourage further research, the implementation code for both our SGE and SGDC modules has been is publicly released at https://github.com/solstice0621/SGDC.

Bo Shi, Wei-ping Zhu, M. N. S. Swamy

Department of Electrical and Computer Engineering, Concordia University

Medical Image SegmentationDynamic ConvolutionBoundary-Aware SegmentationSkin Lesion SegmentationHistopathology Image Segmentation
AskPDF
Feb 26, 2026

SpectralMamba-UNet: Frequency-Disentangled State Space Modeling for Texture-Structure Consistent Medical Image Segmentation

Accurate medical image segmentation requires effective modeling of both global anatomical structures and fine-grained boundary details. Recent state space models (e.g., Vision Mamba) offer efficient long-range dependency modeling. However, their one-dimensional serialization weakens local spatial continuity and high-frequency representation. To this end, we propose SpectralMamba-UNet, a novel frequency-disentangled framework to decouple the learning of structural and textural information in the spectral domain. Our Spectral Decomposition and Modeling (SDM) module applies discrete cosine transform to decompose low- and high-frequency features, where low frequency contributes to global contextual modeling via a frequency-domain Mamba and high frequency preserves boundary-sensitive details. To balance spectral contributions, we introduce a Spectral Channel Reweighting (SCR) mechanism to form channel-wise frequency-aware attention, and a Spectral-Guided Fusion (SGF) module to achieve adaptively multi-scale fusion in the decoder. Experiments on five public benchmarks demonstrate consistent improvements across diverse modalities and segmentation targets, validating the effectiveness and generalizability of our approach.

Fuhao Zhang, Lei Liu, Jialin Zhang +2

College of Computer Science, Sichuan Normal University, Chengdu, Sichuan 610068, China · Zhejiang University & Ant Group, Hangzhou, Zhejiang, China · State Key Laboratory of Public Big Data, Guizhou University, Guiyang, Guizhou 550025, China

Medical Image SegmentationVisual SSMsFrequency-Domain Feature LearningBoundary-Aware SegmentationMulti-Scale Feature Fusion
AskPDF
Feb 26, 2026

A Boundary-Metric Evaluation Protocol for Whiteboard Stroke Segmentation Under Extreme Imbalance

The binary segmentation of whiteboard strokes is hindered by extreme class imbalance, caused by stroke pixels that constitute only 1.791.79% of the image on average, and in addition, the thin-stroke subset averages 1.141.14% \pm 0.41% in the foreground. Standard region metrics (F1, IoU) can mask thin-stroke failures because the vast majority of the background dominates the score. In contrast, adding boundary-aware metrics and a thin-subset equity analysis changes how loss functions rank and exposes hidden trade-offs. We contribute an evaluation protocol that jointly examines region metrics, boundary metrics (BF1, B-IoU), a core/thin-subset equity analysis, and per-image robustness statistics (median, IQR, worst-case) under seeded, multi-run training with non-parametric significance testing. Five losses -- cross-entropy, focal, Dice, Dice+focal, and Tversky -- are trained three times each on a DeepLabV3-MobileNetV3 model and evaluated on 12 held-out images split into core and thin subsets. Overlap-based losses improve F1 by more than 20 points over cross-entropy (0.6630.663 vs 0.4380.438, p<0.001p < 0.001). In addition, the boundary metrics confirm that the gain extends to the precision of the contour. Adaptive thresholding and Sauvola binarization at native resolution achieve a higher mean F1 (0.7870.787 for Sauvola) but with substantially worse worst-case performance (F1 =0.452= 0.452 vs 0.5650.565 for Tversky), exposing a consistency-accuracy trade-off: classical baselines lead on mean F1 while the learned model delivers higher worst-case reliability. Doubling training resolution further increases F1 by 12.7 points.

Nicholas Korcynski

Rowan University USA

Image SegmentationThin-Structure SegmentationBoundary-Aware Segmentation
AskPDF
Feb 25, 2026

SAPNet++: Evolving Point-Prompted Instance Segmentation with Semantic and Spatial Awareness

Single-point annotation is increasingly prominent in visual tasks for labeling cost reduction. However, it challenges tasks requiring high precision, such as the point-prompted instance segmentation (PPIS) task, which aims to estimate precise masks using single-point prompts to train a segmentation network. Due to the constraints of point annotations, granularity ambiguity and boundary uncertainty arise the difficulty distinguishing between different levels of detail (eg. whole object vs. parts) and the challenge of precisely delineating object boundaries. Previous works have usually inherited the paradigm of mask generation along with proposal selection to achieve PPIS. However, proposal selection relies solely on category information, failing to resolve the ambiguity of different granularity. Furthermore, mask generators offer only finite discrete solutions that often deviate from actual masks, particularly at boundaries. To address these issues, we propose the Semantic-Aware Point-Prompted Instance Segmentation Network (SAPNet). It integrates Point Distance Guidance and Box Mining Strategy to tackle group and local issues caused by the point's granularity ambiguity. Additionally, we incorporate completeness scores within proposals to add spatial granularity awareness, enhancing multiple instance learning (MIL) in proposal selection termed S-MIL. The Multi-level Affinity Refinement conveys pixel and semantic clues, narrowing boundary uncertainty during mask refinement. These modules culminate in SAPNet++, mitigating point prompt's granularity ambiguity and boundary uncertainty and significantly improving segmentation performance. Extensive experiments on four challenging datasets validate the effectiveness of our methods, highlighting the potential to advance PPIS.

Zhaoyang Wei, Xumeng Han, Xuehui Yu +4

the School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Science (UCAS), Beijing, 100049, China. · School of Automation and Intelligent Sensing, Shanghai Jiao Tong University (SJTU), Shanghai, 200240, China. · the School of Computer Science and Technology, University of Chinese Academy of Science (UCAS), Beijing, 100049, China.

Instance SegmentationWeakly Supervised Image SegmentationBoundary-Aware Segmentation
AskPDF
Jan 7, 2026

G2P: Gaussian-to-Point Attribute Alignment for Boundary-Aware 3D Segmentation

Point cloud segmentation is critical for 3D scene understanding. However, sparse and irregular point distributions provide limited appearance evidence, making geometry-only features insufficient to distinguish objects with similar shapes but distinct appearances e.g., color, texture, and material. We propose Gaussian-to-Point (G2P), which transfers Gaussian attributes from 3D Gaussian Splatting to point clouds for more discriminative and appearance-consistent segmentation. Our G2P addresses the misalignment between optimized Gaussians and original point geometry by establishing point-wise correspondences. By distilling opacity-derived visibility cues, we mitigate the geometric ambiguity that limits existing models. Additionally, Gaussian scale attributes enable precise boundary localization in complex 3D scenes. Extensive experiments demonstrate that our approach achieves competitive performance on standard benchmarks and shows notable improvements on geometrically challenging classes, without pretrained 2D features or language supervision in our segmentation pipeline.

Hojun Song, Chae-yeong Song, Jeong-hun Hong +5

Kyungpook National University · Korea Electronics Technology Institute · Adobe Research +1

3D Point Cloud Segmentation3D Semantic SegmentationBoundary-Aware Segmentation
AskPDF
Jan 2, 2026

Learning to Segment Liquids in Real-world Images

Liquids like water, wine and medicine are everywhere. However, limited attention has been given to the task of segmenting liquids, hindering the ability of robots to safely avoid and interact with them. The segmentation of liquids is difficult because liquids come in diverse appearances and shapes; moreover, they can be both transparent or reflective, taking on arbitrary objects and scenes from their background and surroundings. To take on this challenge, we construct a liquid dataset, LQDS, consisting of 5000 real-world images annotated into 14 distinct classes, and design a novel liquid detection model, LQDM, which leverages cross-attention between a dedicated boundary branch and the main segmentation branch to enhance mask predictions. Extensive experiments demonstrate the effectiveness of LQDM on the testing set of LQDS, outperforming state-of-the-art methods to establish a strong baseline for the semantic segmentation of liquids. We believe that LQDS and LQDM will facilitate future research in liquid segmentation and enable practical applications in robotics. Our dataset and code is released at https://lonaslee.github.io/LQDM/.

Jonas Li, Michelle Li, Luke Liu +2

Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA

Image SegmentationBoundary-Aware SegmentationRobotic PerceptionSemantic Segmentation
AskPDF
Oct 24, 2025

AURASeg: Attention-Guided Upsampling with Residual-Assisted Boundary Refinement for Drivable-Area Segmentation

Free-space segmentation is essential for autonomous robots to identify drivable regions and navigate safely across indoor, outdoor, and road-scene environments. However, conventional encoder-decoder models often recover coarse region masks while losing the fine spatial information needed to localize drivable-area boundaries accurately. We propose Attention-Guided Upsampling with Residual-Assisted Boundary Refinement (AURASeg), a segmentation framework designed to preserve region-level accuracy while improving boundary quality. Built on a ResNet-18 encoder, AURASeg introduces an Attention Progressive Upsampling Decoder (APUD) that progressively combines semantic context with high-resolution spatial detail, together with a Residual Boundary Refinement Module (RBRM) that explicitly refines contour-sensitive features before final prediction. We evaluate AURASeg across indoor simulation, ground-robot imagery, and road-driving benchmarks. The results show that our proposed model remains competitive with established segmentation models on region-level metrics while providing particularly strong boundary localization, including in comparison with boundary-focused methods. Detailed ablations further demonstrate the role of the proposed decoding and refinement modules.

Narendhiran Vijayakumar

Autonomous Driving PerceptionBoundary-Aware SegmentationSemantic Segmentation
AskPDF