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Automatic steel girder inspection system for high‐speed railway bridge using hybrid learning framework
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2024-12-26 , DOI: 10.1111/mice.13409
Tao Xu, Yunpeng Wu, Yong Qin, Sihui Long, Zhen Yang, Fengxiang Guo

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.

中文翻译:


基于混合学习框架的高速铁路桥梁钢梁自动检测系统



高速铁路桥梁的钢梁需要定期检查,以确保桥梁的稳定性,并为铁路运营提供安全的环境。基于无人机 (UAV) 的检查具有很大的潜力,可以通过提供卓越的空中视角和减轻安全问题而成为一种有效的解决方案。遗憾的是,经典的卷积神经网络 (CNN) 模型存在检测精度有限或模型参数冗余的问题,而现有的基于 CNN 的桥梁检测系统仅设计用于单个视觉任务(例如,仅螺栓检测或锈迹解析)。本文开发了一种新的双任务大梁检查网络(即 BGInet),用于从无人机图像中识别大梁上不同类型的表面缺陷。首先,该网络组装了一个高级检测分支,该分支集成了稀疏注意力模块、扩展的高效线性聚合网络和 RepConv,以解决样本稀缺的小目标,完成高效的螺栓缺陷识别。然后,将创新的 U 形显著性解析分支集成到该系统中,以补充检测分支并解析锈迹斑斑区域。顺利地,还开发和组装了一个利用关键无人机飞行参数的像素到真实世界的映射模型来测量生锈区域。最后,在基于无人机的桥梁数据集上进行的广泛实验表明,与当前先进的模型相比,我们的方法实现了更好的检测精度,同时保持了相当高的推理速度。卓越的性能表明该系统可以有效地将无人机图像转化为有用的信息。
更新日期:2024-12-26
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