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Erschienen in:
Image and Signal Sensors for Computing and Machine Vision: Developments to Meet Future Needs Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

26. Computer Vision-Based Monitoring of Ship Navigation for Bridge Collision Risk Assessment

verfasst von : Xiao-Wei Ye, Tao Jin, Peng-Peng Ang

Erschienen in: Machine Vision and Navigation

Verlag: Springer International Publishing

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Abstract

Due to the increase of frequency and weight of commercial ship trips in waterways, bridges are more vulnerable to ship–bridge collision accidents. There are plenty of reports of such cases all over the world, leading to millions of economic losses. For ancient bridges, irreparable damage might come in the sense of cultural value except for economic losses. The development of computer vision-based technology provides an active defense method to prevent damage in advance. This chapter presents a computer vision-based method for ship–bridge collision assessment and warning for an ancient arch bridge across the Beijing–Hangzhou Grand Canal in Hangzhou, China. The structural characteristics and current status of the arch bridge are analyzed. The traffic volume and parameters of passing ships including the velocity and weight are investigated. The water area in both sides of the bridge is divided into three different security districts corresponding to different warning levels. Image processing techniques are exploited to identify the types of ships for tracking, and the risk of ship–bridge collision is assessed.

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Vorheriges Kapitel Development of Design and Training Application for Deep Convolutional Neural Networks and Support Vector Machines
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Metadaten
Titel
Computer Vision-Based Monitoring of Ship Navigation for Bridge Collision Risk Assessment
verfasst von
Xiao-Wei Ye
Tao Jin
Peng-Peng Ang
Copyright-Jahr
2020
Buch
Machine Vision and Navigation

Print ISBN: 978-3-030-22586-5

Electronic ISBN: 978-3-030-22587-2

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-22587-2_26

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