CBAM-based Method in YOLOv7 for Detecting Defective Vacuum Glass Tubes

The vacuum glass tube is one of the most important materials in the physical industry, and the inspection rate of its production is crucial to the production of subsequent products. We propose a CBAM-based target detection method for YOLOv7 to detect defects in transparent glass tubes, which are not easily detectable due to their transparent walls. We replace all pooling layers in YOLOv7 with CBAM to enable it to better grasp target features. The experimental results show that the recall rate for defective product detection reaches 98.34% and the accuracy rate reaches 96.33% in the simulated industrial inspection environment. It can meet the accuracy requirements of detecting defects of transparent glass tubes in industrial sites.