Fabric defect detection algorithm based on DCGAN and improved YOLOv5 Model
In view of the difficulties of traditional fabric defects detection algorithms such as low preci-sion,slow speed and high missing rate,a fabric defects detection algorithm based on deep convolu-tion generative adversation network and improved YOLOv5 was proposed.Firstly,the public fab-ric defects images of Tianchi Alibaba Cloud were enhanced through DCGAN network to establish a relatively complete data set of all kinds of fabric defects samples.Secondly,in order to improve the detection accuracy of the model,CBAM attention mechanism module is introduced into the YOLOv5 model to make the model detection more focused on the defect area,so as to reduce the missed detection rate.Thirdly,the Mish activation function was replaced with the Leaky ReLU function to enhance the generalization ability of the model.Finally,through the comparison experi-ment between the improved model and the original model,it is concluded that the model proposed in this paper has better detection and robustness than other deep learning models.
万方数据
维普
