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Progressively Learning Dynamic Level Set for Weakly Supervised Industrial Defect Segmentation
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DOI：10.1109/TIM.2023.3316271.png)
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摘要
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With continuous advancements in sensor technology and computer vision, automated surface defect detection has become an important problem in the modern manufacturing industry. Although deep-learning-based defect detection methods have proven successful in various inspection tasks, they often require extensive high-quality pixel-level annotations for model training. Industrial defects, characterized by diverse shapes and intricate textures, require manual labeling by skilled inspectors. This study introduces a weakly supervised defect segmentation framework called the progressively learning dynamic level set (PLDL) to overcome these constraints. This framework incorporates two parallel learning modules and a differentiable level set module interconnected through a progressive learning strategy facilitated by an innovative loss function. Using only image-level labels as inputs, the PLDL framework iteratively refines object boundaries, dynamically optimizes the training process, and generates pixel-level outputs. Our method is efficient and consistent, requiring no manual supervision or postprocessing steps. Experimental results on four benchmark datasets representing diverse industrial scenarios reveal that PLDL outperforms recent weakly supervised models. It achieved 76.52%, 85.23%, 85.71%, 84.43%, and 83.17% in mIoU, mPA, precision, recall, and F -measure, respectively, indicating its superior effectiveness in weakly supervised defect segmentation.
Keyword:
Defect detection
image processing
level set theory
semantic segmentation
weakly supervised learning
期刊
IF:
5.9
论文数:
1.9W
被引数:
5.8W
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