Global Context-Based Self-Similarity Feature Augmentation and Bidirectional Feature Fusion for Surface Defect Detection

Surface defect detection is a significant step in industrial production, which is also essential for ensuring the quality of industrial products. At the moment, although the defect detection methods based on computer vision have made great progress, they are still tough to automate the detection due to the challenges of large changes in size, low-contrast, strong background interference, large intraclass difference as well as small interclass difference. To address the above issues, we design a deep learning model that mainly consists of a feature augmentation module (FAM), and a multiscale bidirectional feature fusion module (MBFFM), while introducing deformable convolutions into the backbone. The FAM enhances the defect features and magnifies the feature difference between defects and background by combining global context-based self-similarity and spatial attention, which can improve the detection performance for weak defects. The MBFFM is applied to complete the bidirectional fusion of high-level and low-level features for the overall perception of defects. In addition, we design an edge weight loss (EWL) to emphasize defect regions by increasing their weights in the loss. At the same time, by combining EWL and deformable convolution, the backbone can accurately extract comprehensive and complete defect features. Experiments verify that the mean intersection over union (mIoU) of our method on the four datasets is superior to other state-of-the-art methods (MT, miou of 83.75%; NEU_SEG, miou of 88.84%; DAGM_defect, miou of 86.74%; ROAD, miou of 76.71%).