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A Weakly Supervised Surface Defect Detection Based on Convolutional Neural Network
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DOI：10.1109/ACCESS.2020.2977821.png)
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Abstract
En 中文
Surface defect detection is a critical task in product quality assurance for manufacturing lines. The deep learning-based methods recently developed for defect detection are typically trained using a supervised learning strategy and large defect sample sets. Conventional methods often require additional pixel-level labeling or bounding boxes to predict the location of defects. However, the number of required samples and the time-intensive annotation process limits the practical use of these algorithms. As such, this study proposes a weakly supervised detection framework in which a CNN model is trained to identify surface cracks in motor commutators. The model was trained using small subsets of defect samples (& x007E;5 & x2013;30) and does not require a pre-trained network. This approach consists of localization and decision networks that simultaneously predict both the location and probability of defects. A new loss function was also developed to identify abnormal regions in a sample with accessible image-level labels. A collaboration learning strategy was then applied to utilize the loss function and compensate for imbalances at the pixel level. Experimental results using a small number of image-level training labels from a real industrial dataset exhibited a 99.5 & x0025; recognition accuracy, which is comparable to relevant methods using pixel-level labels.
Keywords:
Task analysis
Training
Image segmentation
Inspection
Labeling
Feature extraction
Supervised learning
Surface defect detection
quality control
weak supervision
convolutional neural network
localization network
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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