Automated visual inspection of multipattern train components using gradient information and feature fusion under the illumination-variant condition

Guo, Nan; Gao, Yang

doi:10.1177/0954409717734397

Cited by 7 publications

(1 citation statement)

References 23 publications

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“…Sun et al 13 used a coarse-fine scheme based on the CNN model to detect on passing freight wagon bogies the target regions of side-frame retainer keys and end-of-axle shaft bolts and their faults. To eliminate the effect of light, Nan and Gao 14 proposed an automated and hierarchical inspection method for the detection of the condition of the brake beam bolt.…”

Section: Introductionmentioning

confidence: 99%

An undercarriage image driven anomaly detection method for metro vehicle based on adversarial memory enhancement

Wang,

Zhang,

Zheng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part F:

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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 on the established dataset demonstrate that the proposed method reduces false negative rates of 92.4%, 92.6%, 74.6%, and 59.1% compared with [Formula: see text], [Formula: see text], GANomaly, and MemAE, respectively.

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Section: Introductionmentioning

confidence: 99%