ST-CA YOLOv5: Improved YOLOv5 Based on Swin Transformer and Coordinate Attention for Surface Defect Detection

Abstract

Surface defect detection plays a crucial role in industrial equipment to ensure industrial safety. With the development of deep learning, a series of deep learning-based surface defect detection algorithms are proposed and achieved significant success. However, the application of the algorithms in real-world scenarios is restricted by computing power and memory resource, resulting in either deployment issues or performance degradation. In order to balance memory consumption and detection accuracy, we propose a novel method named ST-CA YOLOv5 for surface defect detection. Based on YOLOv5, The Swin Transformer Block (ST) is introduced to design the C3STR module, enhancing the ability to capture long-range semantic information. In the prediction head, we present the CAHead structure by utilizing the lightweight attention module, Coordinate Attention (CA), to fuse feature information. With the benefit of the ST and CA module, the model improves the detection ability for small objects and yields better overall detection performance. Extensive experiments conducted on several real-world datasets demonstrate the effectiveness and superiority of the proposed method, compared with the state-of-the-art methods in terms of detection performance.