| Summary: | 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 (~5-30) 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% recognition accuracy, which is comparable to relevant methods using pixel-level labels.
|
|---|
