Anomaly Detection of High-speed Railway Inspection Images Based on Improved Skip-GAN
In response to the problems of unbalanced training samples and complex labeling in the process of high-speed rail flaw detection,this paper proposed an improved Skip-GAN algorithm to perform unsupervised detection of multiple types of defects in inspection images.First,the high-speed railway inspection images were preprocessed,including track slab segmentation and data enhancement,to reduce the influence of drainage ditches and light and dark environments on network reconstructed images.Second,improvements were made to the Skip-GAN structure,including increasing an at-tention mechanism module and modifying the discriminator to a dual auto-encoder structure.Third,the loss function of the convolutional neural network was improved to increase the network ability to reconstruct images.Finally,the normal images in the high-speed rail flaw detection inspection were used as the training sample input model for training,reali-zing end-to-end detection of various defects in inspection images.The experimental results show that the detection model proposed in this paper achieves good detection results for three types of abnormal detection of rail surface damage,miss-ing fasteners,and foreign matters in the track slab in the case of small number of defect samples with the precision,F1,and AUC of the model reaching 0.868,0.821,and 0.842,respectively.
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