Detection of Sealing Surface of Electric Vehicle Electronic Water Pump Housings Based on Lightweight YOLOv8n

Due to the characteristics of large size differences and shape variations in the sealing surface of electric vehicle electronic water pump housings, and the shortcomings of traditional YOLO defect detection models such as large volume and low accuracy, a lightweight defect detection algorithm based on YOLOv8n (You Only Look Once version 8n) is proposed for the sealing surface of electric vehicle electronic water pump housings. First, on the basis of introducing the MoblieNetv3 module, the YOLOv8n network structure is redesigned, which not only achieves network lightweighting but also improves the detection accuracy of the model. Then, DualConv (Dual Convolutional) convolution is introduced and the CMPDual (Cross Max Pooling Dual) module is designed to further optimize the detection model, which reduces redundant parameters and computational complexity of the model. Finally, in response to the characteristics of large size differences and shape variations in sealing surface defects, the Inner-WIoU (Inner-Wise-IoU) loss function is used instead of the CIoU (Complete-IoU) loss function in YOLOv8n, which improves the positioning accuracy of the defect area bounding box and further enhances the detection accuracy of the model. The ablation experiment based on the dataset constructed in this paper shows that compared with the YOLOv8n model, the weight of the proposed model is reduced by 61.9%, the computational complexity is reduced by 58.0%, the detection accuracy is improved by 9.4%, and the mAP@0.5 is improved by 6.9%. The comparison of detection results from different models shows that the proposed model has an average improvement of 6.9% in detection accuracy and an average improvement of 8.6% on mAP@0.5, which indicates that the proposed detection model effectively improves defect detection accuracy while ensuring model lightweighting.