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Efficient matching of Transformer-enhanced features for accurate vision-based displacement measurement
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.autcon.2025.105962
Haoyu Zhang, Stephen Wu, Xiangyun Luo, Yong Huang, Hui Li
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.autcon.2025.105962
Haoyu Zhang, Stephen Wu, Xiangyun Luo, Yong Huang, Hui Li
Computer vision technology and monitoring videos have been employed to obtain structural displacement measurements. Noniterative algorithms are mainly designed for rapid tracking of the motions of individual image points, rather than dense motion fields. Iterative algorithms are limited to estimating motion fields with small amplitudes and require high computation cost to achieve high accuracy. This paper introduces a noniterative method for vision-based measurements that balances speed and density. The method employs an attention-based matching strategy applied to Transformer-enhanced image features. Motion priors and a physics-informed denoising approach are integrated to improve measurement accuracy. Tested on challenging truss and cable-stayed bridge vibration videos, the method demonstrated superior displacement measurement performance compared to conventional approaches. It also achieved greater robustness to brightness changes and partial occlusions while requiring minimal human intervention. This method supports the development of automated and affordable vibration monitoring systems.
中文翻译:
高效匹配 Transformer 增强功能,实现基于视觉的精确位移测量
计算机视觉技术和监控视频已被用于获得结构位移测量。非迭代算法主要用于快速跟踪单个图像点的运动,而不是密集的运动场。迭代算法仅限于估计小振幅的运动场,并且需要高计算成本才能实现高精度。本文介绍了一种基于视觉的测量的非迭代方法,该方法平衡了速度和密度。该方法采用基于 Transformer 增强图像特征的基于注意力的匹配策略。集成了运动先验和基于物理的降噪方法,以提高测量精度。在具有挑战性的桁架和斜拉桥振动视频上进行了测试,与传统方法相比,该方法表现出卓越的位移测量性能。它还实现了对亮度变化和部分遮挡的更强稳健性,同时需要最少的人工干预。这种方法支持开发自动化且经济实惠的振动监测系统。
更新日期:2025-01-17
中文翻译:
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高效匹配 Transformer 增强功能,实现基于视觉的精确位移测量
计算机视觉技术和监控视频已被用于获得结构位移测量。非迭代算法主要用于快速跟踪单个图像点的运动,而不是密集的运动场。迭代算法仅限于估计小振幅的运动场,并且需要高计算成本才能实现高精度。本文介绍了一种基于视觉的测量的非迭代方法,该方法平衡了速度和密度。该方法采用基于 Transformer 增强图像特征的基于注意力的匹配策略。集成了运动先验和基于物理的降噪方法,以提高测量精度。在具有挑战性的桁架和斜拉桥振动视频上进行了测试,与传统方法相比,该方法表现出卓越的位移测量性能。它还实现了对亮度变化和部分遮挡的更强稳健性,同时需要最少的人工干预。这种方法支持开发自动化且经济实惠的振动监测系统。