Can I read An Undercarriage Image Driven Anomaly Detection Method for Metro Vehicle Based on Adversarial Memory Enhancement on EtoBox?

An Undercarriage Image Driven Anomaly Detection Method for Metro Vehicle Based on Adversarial Memory Enhancement by Lei Wang; Kai Zhang; Qing Zheng; Guofu Ding; Weihua Zhang; Dejun Chen; Bin Liu is a Engineering article available to read on EtoBox.

What is An Undercarriage Image Driven Anomaly Detection Method for Metro Vehicle Based on Adversarial Memory Enhancement about?

Anomaly detection is essential to ensure metro vehicles' safe operation. Error reconstruction-based anomaly detection methods have been widely studied because they only need to be trained by normal data and do not require much anomaly data, which is challenging to obtain. However, sometimes the auto-encoder network for error reconstructing “generalizes” so well that it also rebuilds the anomaly well, leading to missed anomaly detection. Therefore, this paper proposes an undercarriage image-driven anomaly detection method for metro vehicles based on adversarial memory enhancement. Firstly, this study performs component segmentation based on YOLOv5 detection results and constructs a component anomaly detection dataset. Secondly, an anomaly detection method based on memory enhancement and adversarial training of encoding-decoding-encoding structure is proposed for component anomaly detection. It enables the auto-encoder to reconstruct the image better. Thirdly, the combined indicator of the difference between potential features and reconstruction error is used as an anomaly indicator for anomaly detection of metro components, reducing the rate of fault misses. The experimental results

Who reads An Undercarriage Image Driven Anomaly Detection Method for Metro Vehicle Based on Adversarial Memory Enhancement?

Author

Lei Wang; Kai Zhang; Qing Zheng; Guofu Ding; Weihua Zhang; Dejun Chen; Bin Liu

Publisher

SAGE Publications

Published

2023

Language

EN

Field

Engineering (Physical Sciences)