关键词: 小目标检测, YOLOv4, 深度学习, 实时检测, 加权K-means, SENet, RFB-s模块

Abstract: Aiming at the problem of insufficient detection accuracy of YOLOv4 in the data set of surface defects of self-built automobile steel parts, this paper proposes a surface defect detection method of automobile steel parts based on improved YOLOv4 by taking the advantage of deep learning. Firstly, the weighted K-means algorithm is used to determine the initial anchors pre-selection box to enhance the matching accuracy of anchors and feature map size and improve the detection efficiency. Then the SE module is introduced into the residual unit of the YOLOv4 backbone network to increase the weight of useful features and suppress the weight of invalid features to improve the detection accuracy. Finally, the RFB-s module is connected to the 76×76 feature map to enhance the feature extraction ability of small target information. Aiming at the single defect detection problem of self-built data set of surface defects of automobile parts, the experimental results show that the improved model improves the detection accuracy of mAP50 by 4.3 percentage points compared with the original YOLOv4 model, and has a better detection effect on small targets. It shows that the improved algorithm can meet the requirements of detection speed and accuracy for specific steel parts surface defect detection, and effectively solve the practical problems. Aiming at COCO data set multi-classification problem, the mAP50 value of the improved model is 0.2 percentage points higher than that of the original YOLOv4, and the FPS value reaches 20, which indicates that the improved algorithm can be migrated to other data sets, and the generalization of the algorithm is verified.

Key words: small target detection, YOLOv4, deep learning, real-time detection, weighted K-means, SENet, RFB-s module