An image-level weakly supervised segmentation method for No-service rail surface defect with size prior

Abstract Surface defect segmentation of no-service rail is important for its quality assessment. Several deep learning methods have obtained good performances based on computer vision testing. However, these methods all require a large number of training samples with pixel-level labels. Marking pixel-level labels is a difficult task – time-consuming, labor-consuming, and professionals. To address the issue, a novel image-level weakly supervised segmentation formulation is proposed for no-service rail surface defects. These defects are decomposed into three sub-categories (strip-shaped, spot-shaped, block-shaped) according to the size prior information (area and shape). Then, a method is presented with a pooling combination module. The pooling combination module makes full use of the size attributes of the sub-category by utilizing different pooling functions for different sub-categories. Experimental results demonstrate that our method is effective and outperforms the state-of-the-art methods. Index terms - Surface defect segmentation; no-service rail; image-level weakly supervised; size prior information; pooling combination module.

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