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Titlebook: Deep Learning-Based Detection of Catenary Support Component Defect and Fault in High-Speed Railways; Zhigang Liu,Wenqiang Liu,Junping Zhon

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发表于 2025-3-21 18:07:40 | 显示全部楼层 |阅读模式
书目名称Deep Learning-Based Detection of Catenary Support Component Defect and Fault in High-Speed Railways
编辑Zhigang Liu,Wenqiang Liu,Junping Zhong
视频video
概述Focuses on the Deep Learning technologies and applications in catenary detection of high-speed railways.Presents the up-to-date research results of the catenary detection.Adopts and improves the advan
丛书名称Advances in High-speed Rail Technology
图书封面Titlebook: Deep Learning-Based Detection of Catenary Support Component Defect and Fault in High-Speed Railways;  Zhigang Liu,Wenqiang Liu,Junping Zhon
描述.This book focuses on the deep learning technologies and their applications in the catenary detection of high-speed railways. As the only source of power for high-speed trains, the catenary‘s service performance directly affects the safe operation of high-speed railways. This book systematically shows the latest research results of catenary detection in high-speed railways, especially the detection of catenary support component defect and fault. Some methods or algorithms have been adopted in practical engineering. These methods or algorithms provide important references and help the researcher, scholar, and engineer on pantograph and catenary technology in high-speed railways. Unlike traditional detection methods of catenary support component based on image processing, some advanced methods in the deep learning field, including convolutional neural network, reinforcement learning, generative adversarial network, etc., are adopted and improved in this book. The main contents include the overview of catenary detection of electrified railways, the introduction of some advance of deep learning theories, catenary support components and their characteristics in high-speed railways, the
出版日期Book 2023
关键词High-speed Railway; Pantograph-Catenary System; Automatic Detection; Image Processing; Deep Learning; Con
版次1
doihttps://doi.org/10.1007/978-981-99-0953-7
isbn_softcover978-981-99-0955-1
isbn_ebook978-981-99-0953-7Series ISSN 2363-5010 Series E-ISSN 2363-5029
issn_series 2363-5010
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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发表于 2025-3-21 23:03:07 | 显示全部楼层
https://doi.org/10.1007/978-3-531-90356-9 the natural environment for a long time and is easily affected by the dirt in the environment. The catenary components in the tunnel are also vulnerable to the long-term impact of tunnel dust. It causes their state characteristics to be submerged by noise and interference, which will increase the d
发表于 2025-3-22 02:43:40 | 显示全部楼层
https://doi.org/10.1007/978-3-531-90356-9extract handcrafted features (e.g., SIFT, SURF, and HoG) of the template component image and global catenary image and then adapt the feature-matching approach to locate the target component. These methods can only locate one class component at a time and have low accuracy and efficiency. When the b
发表于 2025-3-22 06:52:52 | 显示全部楼层
发表于 2025-3-22 10:41:50 | 显示全部楼层
Deutsche Außenwirtschaftsförderungn the high-speed train passes quickly, the whole device is continuously shocked. Many components of the cantilever system are easily loosened, resulting in structural deformation of the entire cantilever system.
发表于 2025-3-22 15:22:23 | 显示全部楼层
发表于 2025-3-22 18:17:33 | 显示全部楼层
Die Entwicklung der deutschen Europapolitik,ing, and singular value detection. Although these methods can be formulated mathematically using expert knowledge, which is interpretable, they only perform well for some specific conditions and cannot fit well with variable scenarios.
发表于 2025-3-22 21:47:05 | 显示全部楼层
发表于 2025-3-23 04:49:17 | 显示全部楼层
Deutsche Außenwirtschaftsförderungn the high-speed train passes quickly, the whole device is continuously shocked. Many components of the cantilever system are easily loosened, resulting in structural deformation of the entire cantilever system.
发表于 2025-3-23 06:13:20 | 显示全部楼层
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