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SCA-GANomaly: an unsupervised anomaly detection model of high-speed railway catenary components

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Abstract

With the rapid development of high-speed railways, the safety inspection requirements for supporting devices of high-speed railway catenary components have become increasingly stringent. Currently, defect detection tasks for catenary equipment heavily rely on manual judgment, which is labor-intensive, inefficient, and prone to missing defects. Furthermore, high-speed railway catenary components present a myriad of challenges, including varying sizes, multiple defect types, and a limited number of defects. This complexity makes it difficult to address these issues using a single approach, and Few-Shot defect detection tasks are particularly challenging to achieve using deep learning methods. In this article, we propose a novel SCA-GANomaly model based on the GANomaly network, specifically designed to overcome the challenges of few-shot defect detection by utilizing only normal high-speed railway catenary images for training. The model incorporates selective skip connections and hybrid attention mechanisms, and the loss function is optimized using Earth Mover(EM) Distance, effectively improving image reconstruction and enhancing training stability for high-speed railway catenary images. We conduct comprehensive experiments and evaluations on three types of components: Insulators, Oblique Bracing Wires(OBW), and Puller bolts. The results demonstrate the excellent generalization capabilities of our proposed model, significantly outperforming the GANomaly model in terms of defect detection performance. Specifically, the AUC values for Insulator defect detection increased by 0.15, OBW defect detection by 0.10, and Puller bolts defect detection by 0.12.

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Data availability

The dataset used in this study contains sensitive information and therefore its data are subject to strict confidentiality. To maintain the confidentiality and security of the data, we are unable to share access to this dataset publicly.

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Acknowledgements

This work was supported by the 2022 Hunan Provincial Natural Science Foundation Project (2022JJ60075): Research on Transient Protection Technology and Application of Rail Transit Based on Dynamic Load Characteristics.

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  1. Department of Information Engineering, Dalian University, Dalian, 116622, Liaoning, China

    Shijie Wang, Qijie Zou & Bing Gao

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  1. Shijie Wang
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Correspondence to Qijie Zou.

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Wang, S., Zou, Q. & Gao, B. SCA-GANomaly: an unsupervised anomaly detection model of high-speed railway catenary components. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19011-3

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