生成对抗网络在表面缺陷生成中的应用OA

The object detection algorithm based on deep learning is widely used in the field of industrial product surface defect detection,but the constructed model requires a large amount of labeled product defect data.To reduce the cost of obtaining defect data,this paper proposes a surface defect generation algorithm based on generative adversarial networks.Through this algorithm,defect data that is closer to the true distribution can be generated.The experimental results show that the generated defect images are very realistic and contain a certain degree of discriminability.Using them as samples to participate in defect detection model training can produce regularization effects,thereby improving the robustness and generalization ability of defect detection.