Enriched multi-scale cascade pyramid features and guided context attention network for industrial surface defect detection

Surface defect detection is a very important technique to guarantee product quality in industrial fields. However, the detection of multi-scale defects and defects with poor visibility is still a challenging problem. To address this issue, we propose a novel network by collaborating multi-scale cascade pyramid features and a guided context attention mechanism for the pixel-wise defection of surface defects, called MPA-Net. The MPA-Net is a full y-convolutional network (FCN) with an encoder–decoder architecture, which can integrate multi-scale features and merge them into the different stages of the decoder for generating the defect segmentation map. Specifically, the proposed guided context attention module (GCA) is used to transmit the global context information from the large scale to the small scale, which can promote the initial recovery capability of the decoder, and thus help to locate defects with different sizes and defects with poor visibility. Moreover, the proposed pyramid feature fusion and enrichment module (FFEM) is employed to aggregate low-level coarse features and high-level semantic features in each scale, so as to increase the ability of defect feature representation. The aggregation features at different scales are then fused to the different layers of the decoder, which is beneficial to recover the details of defects gradually. The evaluation results on four public datasets demonstrate that the proposed method has excellent performances on mean intersection of union (DAGM2007: 64.94%, KolektorSSD: 77.90%, RSDDs-I: 86.63%, RSDDs-II: 80.62%, FID: 96.98%) and mean pixel accuracy (DAGM2007: 67.97%, KolektorSSD: 85.01%, RSDDs-I: 94.13%, RSDDs-II: 88.53%, FID: 98.71%).