DA-YOLO: A dual attention-driven YOLOv11 framework for multi-scale fine-grained metal surface defect detection

The detection of metal surface defects is crucial in the field of industrial production. However, in practical applications, challenges such as ambiguous defect directions, large scale differences, and strong background interference are often encountered. This paper proposes an improved multi-scale fine-grained object detection framework based on YOLOv11, referred to as “DA-YOLO.” Firstly, the 3D attention module (3DAM) was used to enhance the model’s ability to model spatial direction features and improve the model’s ability to perceive fine-grained structures. Secondly, a feature enhancement module (AMFEM) employing multi-scale convolution and a spatial-channel attention mechanism was constructed, significantly boosting the model’s recognition accuracy for multi-scale targets and blurry boundary defects. Furthermore, an intersection and union ratio Aware Joint Loss function (IoU-Aware joint loss, IAJ-Loss) was proposed and designed, which further enhanced the quality perception ability and stability of the model in complex detection scenarios. The experimental results show that the DA-YOLO model improved mAP@0.5 by 5.42% and 4.05% respectively on the GC10-DET and NEU-DET datasets compared to the baseline YOLOv11 model, demonstrating superior defect detection performance.