Transparent Component Defect Detection Method Based on Improved YOLOv7 Algorithm

Abstract

Transparent components such as glass and fiber-reinforced plastics are widely used in engineering practice, which are prone to generate defects, and change its surface and internal structure, and cause great risks to the performance and stability of products. To solve the above problems, firstly, we studied the defect detection of transparent components, proposed an improved YOLOv7 (You Only Look Once V7) algorithm, replaced the Loss function CIoU (Complete-Intersection over Union) of the network model with Wise-IoU (Wise-Integration Over Union), and raised its convergence performance. Secondly, Global Attention Mechanism (GAM) is embedded in the backbone, and a dynamic target head frame is used in the output layer to generate the standard head frame and the attention function, improving the network’s attention to micro defects and ensuring the detection accuracy of micro defects. Thirdly, an intelligent defect detection platform was designed by combining mechanical engineering, visual perception, information processing and other technologies, and 150 rounds of comparative ablation experiments were conducted on typical transparent components. The improved algorithm has raised 2.6% in Mean Average Precision (MAP) value compared to the original algorithm. The improved model has better detection performance for micro defects and higher recognition accuracy. It can effectively screen out the location and category of defects, and eliminate defective components, which is consistent with the actual engineering situation. It satisfies the actual needs of product quality testing in the production process and provides reference experience for the industrial use of defect detection methods.