SDF-YOLO: an efficient neural network for detecting surface defects in steel strips

Abstract Traditional steel strip defect detection methods face challenges such as low accuracy and slow efficiency, failing to achieve the high standards of modern industry. To handle these issues, this study proposes SDF-YOLO, an efficient and accurate defect detection method based on the YOLOv8 algorithm, designed to enhance detection performance and optimize production processes. By integrating Shuffle Attention into the Spatial Pyramid Pooling network, the method improves contextual understanding; combining Deformable Convolution DCNv2 with the C2f module enhances the detection of overlapping defects while replacing CIoU with Focal-SIoU increases the precision of bounding box shape and localization. Results from experiments conducted on the NEU-DET dataset demonstrate that SDF-YOLO achieves an accuracy of 76.8%, mAP50 by 76.9%, and mAP50-95 of 45%, outperforming YOLOv8n by 7.9%, 2.4%, and 1.4%, respectively. Additionally, its application on the GC-DET dataset demonstrates improvements in mAP50 by 2.3%, confirming its enhanced generalization and significantly improved detection capabilities for overlapping defects compared to YOLOv8n.