Automatic steel girder inspection system for high-speed railway bridge using hybrid learning framework

IF 8.5 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Tao Xu, Yunpeng Wu, Yong Qin, Sihui Long, Zhen Yang, Fengxiang Guo
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引用次数: 0

Abstract

The steel girder of high-speed railway bridges requires regular inspections to ensure bridge stability and provide a safe environment for railway operations. Unmanned aerial vehicle (UAV)-based inspection has great potential to become an efficient solution by offering superior aerial perspectives and mitigating safety concerns. Unfortunately, classic convolutional neural network (CNN) models suffer from limited detection accuracy or redundant model parameters, and existing CNN-based bridge inspection systems are only designed for a single visual task (e.g., bolt detection or rust parsing only). This paper develops a novel bi-task girder inspection network (i.e., BGInet) to recognize different types of surface defects on girder from UAV imagery. First, the network assembles an advanced detection branch that integrates the sparse attention module, extended efficient linear aggregation network, and RepConv to solve the small object with scarce samples and complete efficient bolt defect identification. Then, an innovative U-shape saliency parsing branch is integrated into this system to supplement the detection branch and parse the rust regions. Smoothly, a pixel-to-real-world mapping model utilizing critical UAV flight parameters is also developed and assembled to measure rust areas. Finally, extensive experiments conducted on the UAV-based bridge girder dataset show our method achieves better detection accuracy over the current advanced models yet remains a reasonably high inference speed. The superior performance illustrates the system can effectively turn UAV imagery into useful information.
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来源期刊
CiteScore
17.60
自引率
19.80%
发文量
146
审稿时长
1 months
期刊介绍: Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms. Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.
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