The accurate tracking of vehicle loads is essential for the condition assessment of bridge structures. In recent years, a computer vision method that is based on multiple sources of data from monitoring cameras and weight-in-motion (WIM) systems has become a promising strategy in bridge vehicle load identification for structural health monitoring (SHM) and has attracted increasing attention. The implementation of vehicle re-identification, namely, the identification of the same vehicle from images that were captured at different locations or time instants, is the key topic of this study. In this study, a vehicle re-identification method that is based on HardNet, a deep convolutional neural network (CNN) specialized in picking up local image features, is proposed. First, we obtain the vehicle point feature positions in the image through feature detection. Then, the HardNet is employed to encode the point feature image patches into deep learning feature descriptors. Re-identification of the target vehicle is achieved by matching the encoded descriptors between two images, which are robust toward scaling, rotation, and other types of noises. A comparison study of the proposed method with three published vehicle re-identification methods is performed using vehicle image data from a real bridge, and the superior performance of our proposed method is demonstrated.

车辆载荷的准确跟踪对于桥梁结构的状况评估至关重要。近年来，基于来自监控摄像头和动态称重 (WIM) 系统的多个数据源的计算机视觉方法已成为用于结构健康监测 (SHM) 的桥梁车辆载荷识别的一种有前景的策略，并受到越来越多的关注。注意力。车辆重新识别的实施，即从不同位置或时间捕获的图像中识别同一辆车，是本研究的关键主题。在这项研究中，提出了一种基于 HardNet 的车辆重新识别方法，HardNet 是一种专门用于拾取局部图像特征的深度卷积神经网络 (CNN)。第一的，我们通过特征检测得到图像中的车辆点特征位置。然后，使用 HardNet 将点特征图像块编码为深度学习特征描述符。目标车辆的重新识别是通过匹配两个图像之间的编码描述符来实现的，这些描述符对缩放、旋转和其他类型的噪声具有鲁棒性。使用来自真实桥梁的车辆图像数据对所提出的方法与三种已发布的车辆重新识别方法进行了比较研究，并证明了我们提出的方法的优越性能。旋转和其他类型的噪音。使用来自真实桥梁的车辆图像数据对所提出的方法与三种已发布的车辆重新识别方法进行了比较研究，并证明了我们提出的方法的优越性能。旋转和其他类型的噪音。使用来自真实桥梁的车辆图像数据对所提出的方法与三种已发布的车辆重新识别方法进行了比较研究，并证明了我们提出的方法的优越性能。