Intelligent Generation of Rail Surface Defect Images Based on CycleGAN and Pix2Pix
In order to solve the small-sample learning problem of highly dynamic and high-precision track inspection technology based on artificial intelligence,two generative adversarial network models based on CycleGAN and Pix2Pix were proposed to achieve semantic feature learning of small-sample datasets and intelligent generation of rail surface de-fect data.The Pix2Pix model generated class-specific rail surface images while the CycleGAN model converted defect-free rail surface images into defective rail surface images,with the defect style not constrained by the stereotype.In this way,a large-scale enhancement of the rail surface defect dataset was achieved on the basis of maintaining the same cate-gory of rail surface defects with different forms,solving the problems of imbalanced data distribution in small sample datasets,lack of diversity in data,and high difficulty in data annotation.Performance experiments using VGG19,YOLOv5 and UNet show that the accuracy of the rail surface defect image enhancement dataset generated in this paper is 81.177％in the image classification task,23.138％better than the original dataset.In the target detection task,the ac-curacy is 91.90％,up 26.60％,with respect to the recall rate of 87.20％,an increase of 16.00％,and mean average precision of 93.50％,an improvement of 18.30％.In the semantic segmentation task,the Dice score is 71.015,an im-provement of 6％over the original dataset.The research results demonstrate important application value in solving the small sample learning problem of track inspection technology.
万方数据
维普
