Defect Identification of Adhesive Structure Based on DCGAN and YOLOv5

To overcome the problem of fewer sample and uneven distribution of defect type in defect detection of adhesive structure parts, a defect identification approach based on DCGAN and YOLOv5 is proposed. The above problems are solved by fine-tuning the structure and loss function of DCGAN, the generated high-quality defect images and the extended defect dataset are the basis for accurate identification with YOLOv5. The EIOU loss function is utilized in the YOLOv5 network, the mAP value and recall increase by 3.9% and 10.5% compared with the GIOU loss function, but the precision decreases. To solve this problem, the feature extraction capability of the network is enhanced by incorporating the CBAM after the C3 module in the YOLOv5 network. The mAP, precision, and recall of the optimized YOLOv5 algorithm are improved to 78.6%, 77.2%, and 76%, respectively, the precision compared to the original model improved by 10.6%. The results demonstrate that the improved YOLOv5 model can effectively identify defects of adhesive structure.