Automotive air conditioning vent defect detection method based on improved yolov5

In response to the low efficiency and high miss rate in the manual quality inspection of automotive air vent trim rings by current automotive suppliers, this paper proposes an improved YOLOv5-based method for detecting defects in automotive air vent trim rings. Firstly, the Coordinate Attention(CA) is introduced into the network to improve the feature extraction capability of the network for defects in automotive air vent trim rings. Secondly, the Space-to-Depth(SPD) module is introduced for downsampling to reduce the loss of subtle features during the downsampling process, which is beneficial for improving the feature extraction of the network for trim ring defects. Finally, the detection head is replaced with a decoupling head to alleviate the conflict between the classification task and the regression task, thereby improving the defect detection rate. On a self-generated dataset of defects in automotive air conditioning vent trim rings, the improved network achieves a mean accuracy(mAP) of 93.6%, an accuracy of 91.3%, and a recall rate of 88. 9%. Compared with the original network, the precision is improved by 3.4%, the accuracy is improved by3.2%,and the recall rate is improved by 3%.The proposed method can achieve defect detection in automotive air conditioning vent trim rings, laying a foundation for building an automated quality inspection system for automotive air conditioning vent trim rings.