BHE‐YOLO: Effective Small Target Detector for Aluminum Surface Defect Detection

Abstract Aluminum surface defect detection is a critical task to make location and classification predictions about defects in the industrial production process. However, defects complexity and the requirement of rapid production have led to a great challenge for existing detection algorithms. In this work, an effective small target detector named BHE‐YOLO is proposed for aluminum surface defect detection. First, the BiFPN is modified and integrated with YOLOv5s to achieve effective weighted feature fusion and cross‐scale connection. Second, the Hard Swish activation function is applied to better extract defect feature information. Third, the Equalized Focal Loss function is introduced to replace the cross‐entropy formula of the negative sample confidence part of the loss function. Finally, the experiments are carried out on the aluminum profile defect dataset of the Aliyun Tianchi Competition. The results demonstrate that BHE‐YOLO has excellent performance in terms of Precision, Recall, F1 score, and mAP, and its superiority over the typical detection algorithms, especially for small target defects of aluminum surface.