Defect Detection in High-Speed Railway Overhead Contact System: Importance, Challenges, and Methods

Yao, Liyi; Qiu, Jianhui; Gao, Shaobing; Du, Xinyu; Gu, Zichen; Song, Haoran

doi:10.1109/spac53836.2021.9539958

Cited by 1 publication

(1 citation statement)

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“…In these methods, most of them focus on how to detect anomalies and identify surface defects of key components. Currently, inspired by the success of convolutional neural networks (CNNs) on image analysis, more and more AD algorithms are equipped with CNNs to meet the requirement of fast computing speed, high efficiency, and detecting accuracy [18][19][20][21][22][23][24][25][26][27]. According to the image analysis types, the popular AD methods for high-speed train safety inspection can be divided into four categories, unsupervised generated methods [7,8,11,12], anomaly or defect classification [17,22,25,[28][29][30], abnormal object detection [9, 10, 18-21, 23, 26], and defect segmentation [29,[31][32][33].…”

Section: Introductionmentioning

confidence: 99%

AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection

2022

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This paper proposed a novel anomaly detection (AD) approach of high-speed train images based on convolutional neural networks and the Vision Transformer. Different from previous AD works, in which anomalies are identified with a single image using classification, segmentation, or object detection methods, the proposed method detects abnormal difference between two images taken at different times of the same region. In other words, we cast anomaly detection problem with a single image into a difference detection problem with two images. The core idea of the proposed method is that the “anomaly” commonly represents an abnormal state instead of a specific object, and this state should be identified by a pair of images. In addition, we introduced a deep feature difference AD network (AnoDFDNet) which sufficiently explored the potential of the Vision Transformer and convolutional neural networks. To verify the effectiveness of the proposed AnoDFDNet, we gathered three datasets, a difference dataset (Diff dataset), a foreign body dataset (FB dataset), and an oil leakage dataset (OL dataset). Experimental results on the above datasets demonstrate the superiority of the proposed method. In terms of the F1-score, the AnoDFDNet obtained 76.24%, 81.04%, and 83.92% on Diff dataset, FB dataset, and OL dataset, respectively.

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