AP-Net: a metallic surface defect detection approach with lightweight adaptive attention and enhanced feature pyramid

Surface defect detection is essential for ensuring the quality of metallic products. Many excellent surface defect detectors have been designed in recent years. Most detection methods achieve success by using attention and feature pyramid modules. However, most attention modules consider only simple global information in channel features and incur heavy computational costs. Furthermore, the existing feature pyramids fail to effectively utilize the information from all multi-level features. To alleviate these issues, we design a metallic surface defect detector, named AP-Net. Specifically, we propose a lightweight adaptive attention module (LAA) and an enhanced feature pyramid module (EFP). We perform extensive experiments on three datasets. The experimental results show that the detection accuracies of the representative detectors can be significantly improved using our LAA and EFP.