Bridge weigh-in-motion using bridge influence surface and computer vision: an experimental study

Xudong Jian, Jiwei Zhong, Yafei Wang, Ye Xia, Limin Sun
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Abstract

Complicated traffic scenarios, including random lane change and multiple presences of vehicles on bridges are the main obstacles preventing bridge weigh-in-motion (BWIM) technique from reliable and massive application. To tackle the complicated traffic problems of BWIM, this paper develops a novel BWIM method by integrating the bridge influence surface theory and deep-learning based computer vision technique. For illustration and verification, the proposed method is applied to identify gross weights of vehicles in scale experiments, where various complicated traffic scenarios are simulated. Identification results confirm the favourable robustness, accuracy, and cost- effectiveness of the method.
基于桥梁影响面和计算机视觉的桥梁动态称重实验研究
复杂的交通场景,包括随机变道和桥梁上的多个车辆存在,是阻碍桥梁运动称重(BWIM)技术可靠大规模应用的主要障碍。为了解决BWIM中复杂的交通问题,本文将桥梁影响面理论与基于深度学习的计算机视觉技术相结合,提出了一种新的BWIM方法。为了说明和验证,将该方法应用于模拟各种复杂交通场景的车辆毛重识别实验。辨识结果证实了该方法具有良好的鲁棒性、准确性和成本效益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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