Unlimited

Authorized users

Can lend chapters, not whole ebooks

Deep learning-based detection of catenary support component defect and fault in high-speed railways / Zhigang Liu, Wenqiang Liu, Junping Zhong.

9789819909537 electronic book
9819909538 electronic book
9789819909520
981990952X

Singapore : Springer, 2023.

English

1 online resource (xiii, 239 pages) : illustrations (some color).

10.1007/978-981-99-0953-7 doi

TF1460 .L58 2023

625.1

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 image reprocessing of catenary support components, the positioning of catenary support components, the detection of defect and fault, the detection based on 3D point cloud, etc.

Includes bibliographical references.

Access limited to authorized users.

Online resource; title from PDF title page (SpringerLink, viewed April 19, 2023).

Advances in high-speed rail technology, 2363-5029

Print version: 9789819909520

Online Resources > Ebooks
All Resources