Region-Aware CAM: High-Resolution Weakly-Supervised Defect Segmentation via Salient Region Perception
Journal:
arXiv
Published Date:
Jun 28, 2025
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.
