In the complex texture of the tile surface, there are more low-visibility small defects, and the interference from the complex textured background is serious. This results high false detection and false alarm rate using traditional object detection methods. To enhance the efficiency of defect detection, this paper proposed a defect detection approach on complex textured tile surfaces based on the joint attention mechanisms of channels and spatial. Firstly, to enhance the feature expression of small defects, it proposed a selective feature fusion method by modeling the relationship between deep and shallow feature channels. Secondly, it designed a joint channel and spatial attention module that selected key feature channels and suppressed texture regions through channel and spatial attention, enabling the model to focus on learning defect features and enhancing its ability to discriminate between defects and texture. Finally, it validated the approach on a dataset of complexly textured ceramic tile surface defects. The experimental results demonstrate that compared to the AFF and CBAM methods, the selective feature fusion method and channel & spatial joint attention achieved improvements of 5.3 AP and 6.32 AP, respectively. In addition, this paper compared the overall approach with the existing tile detection method YOLOv5 and texture fabric defect detection method AFAM. The results show that it outperforms these methods, with respective improvements of 1.32 AP and 2.12 AP.

[2023-12-08] Accepted Paper /doc/2023.06.0303/v1

[2024-03-05] Printed Article /doc/2023.06.0303/v2