Detection Method of Auto Parts Defects Based on Improved YOLOV7
The defect detection of automotive components usually adopts a deep learning model based on YO-LOV7.In response to the problem of limited computing resources in the production environment,an improved YOLOV7 surface defect detection method for automotive components is proposed.Firstly,the lightweight net-work model MobileNetV3 is used to replace the YOLOV7 backbone feature extraction network.Secondly,the neck network is replaced with a repeated weighted bidirectional feature pyramid network BiFPN and a multi-path efficient design BepC3 module.Finally,a regression loss function based on Gaussian Wasserstein distance is used to replace the original loss function.The experimental results show that the algorithm improves the accuracy of AP50 by 0.155 compared to the traditional YOLOV7 and 0.139 compared to the YOLOx algorithm.It performs excellently in both detection accuracy and efficiency.
part defect detectionYOLOV7Mobile NetV3data enhancementlightweight network
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