A domain adaptation YOLOv5 model for industrial defect inspection

Abstract

In the process of industrial production, manufactured products are prone to surface defects for a variety of reasons. To overcome the problem of high time cost and the strong demand for large sample data sets, a detector based on transfer learning is commonly utilized. In this paper, a domain adaptation YOLOv5 model, named DAYOLOv5, is proposed for automatic surface defect inspection. The hyperparameter α in DAYOLOv5 for knowledge transfer can be designed specially to achieve better generalization in real-world industrial applications. Meanwhile, in the field of magnetic tile surface defect detection, our DAYOLOv5 outperforms traditional mixed training and pretrain-finetune methods with limited data sets and has great robustness. Overall, the experimental results demonstrate that our DAYOLOv5 model can indeed boost performance on small-scale target data sets and is applicable to practical industrial scenarios.