A deep neural network-based vehicle re-identification method for bridge load monitoring

Abstract

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.

Identification and Tracking of Vehicles between Multiple Cameras on Bridges Using a YOLOv4 and OSNet-Based Method

“…The vehicle re-identification between multiple cameras is a hot field in computer vision [ 25 , 26 , 27 ]. The number of re-identification studies has grown in number, aiming to solving the challenge of matching objects across different cameras when the primary hallmark, such as the face or plate number, is unrecognized.…”

“…The vehicle re-identification between multiple cameras is a hot field in computer vision [25][26][27]. The number of re-identification studies has grown in number, aiming to To overcome the existing problem, Chen et al [23] applied feature and area-based approaches to re-identify vehicles between multiple cameras.…”