Region-Aware CAM: High-Resolution Weakly-Supervised Defect Segmentation via Salient Region Perception

• URL: http://arxiv.org/abs/2506.22866v1
• Date: Sat, 28 Jun 2025 12:24:45 GMT
• Title: Region-Aware CAM: High-Resolution Weakly-Supervised Defect Segmentation via Salient Region Perception
• Authors: Hang-Cheng Dong, Lu Zou, Bingguo Liu, Dong Ye, Guodong Liu,
• Abstract summary: This paper proposes a novel weakly supervised semantic segmentation framework.<n>It consists of a region-aware class activation map (CAM) and pseudo-label training.<n>The proposed framework effectively bridges the gap between weakly supervised learning and high-precision defect segmentation.
• Score: 2.9962030276180758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surface defect detection plays a critical role in industrial quality inspection. Recent advances in artificial intelligence have significantly enhanced the automation level of detection processes. However, conventional semantic segmentation and object detection models heavily rely on large-scale annotated datasets, which conflicts with the practical requirements of defect detection tasks. This paper proposes a novel weakly supervised semantic segmentation framework comprising two key components: a region-aware class activation map (CAM) and pseudo-label training. To address the limitations of existing CAM methods, especially low-resolution thermal maps, and insufficient detail preservation, we introduce filtering-guided backpropagation (FGBP), which refines target regions by filtering gradient magnitudes to identify areas with higher relevance to defects. Building upon this, we further develop a region-aware weighted module to enhance spatial precision. Finally, pseudo-label segmentation is implemented to refine the model's performance iteratively. Comprehensive experiments on industrial defect datasets demonstrate the superiority of our method. The proposed framework effectively bridges the gap between weakly supervised learning and high-precision defect segmentation, offering a practical solution for resource-constrained industrial scenarios.

Related papers

• Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift [51.24522135151649]
  Anomaly detection plays a crucial role in quality control for industrial applications.<n>Existing methods attempt to address domain shifts by training generalizable models.<n>Our proposed method demonstrates superior results compared with state-of-the-art anomaly detection and domain adaptation methods.
  arXiv  Detail & Related papers  (2025-03-19T05:25:52Z)
• EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models [23.898938659720503]
  Industrial Anomaly Detection (IAD) is critical to ensure product quality during manufacturing.<n>We propose a novel approach that introduces a dedicated multi-modal defect localization module to decouple the dialog functionality from the core feature extraction.<n>We also contribute to the first multi-modal industrial anomaly detection training dataset, named Defect Detection Question Answering (DDQA)
  arXiv  Detail & Related papers  (2025-03-18T11:33:29Z)
• Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection [4.691083532629246]
  We propose Vision-Language Anomaly Detection via Contrastive Cross-Modal Training (CLAD)<n> CLAD aligns visual and textual features into a shared embedding space using contrastive learning.<n>We demonstrate that CLAD outperforms state-of-the-art methods in both image-level anomaly detection and pixel-level anomaly localization.
  arXiv  Detail & Related papers  (2024-12-01T17:00:43Z)
• Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection [12.417754433715903]
  We introduce Sparse Semi-DETR, a novel transformer-based, end-to-end semi-supervised object detection solution. Sparse Semi-DETR incorporates a Query Refinement Module to enhance the quality of object queries, significantly improving detection capabilities for small and partially obscured objects. On the MS-COCO and Pascal VOC object detection benchmarks, Sparse Semi-DETR achieves a significant improvement over current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-04-02T10:22:23Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Decoupled DETR: Spatially Disentangling Localization and Classification for Improved End-to-End Object Detection [48.429555904690595]
  We introduce spatially decoupled DETR, which includes a task-aware query generation module and a disentangled feature learning process. We demonstrate that our approach achieves a significant improvement in MSCOCO datasets compared to previous work.
  arXiv  Detail & Related papers  (2023-10-24T15:54:11Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Frequency Perception Network for Camouflaged Object Detection [51.26386921922031]
  We propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain.<n>Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage.<n>Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-17T11:30:46Z)
• Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation [62.29076080124199]
  This paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains.
  arXiv  Detail & Related papers  (2020-03-23T13:40:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.