A Benchmark Data Set for Vision-Based Traffic Load Monitoring in a Cable-Stayed Bridge

Traffic load monitoring based on deep learning and computer vision has garnered significant attention in bridge engineering worldwide. Unlike traditional traffic load monitoring systems, computer vision-based techniques can accurately extract the spatiotemporal load distribution across the entire bridge in an autonomous manner. However, many of the related studies in the literature used data sets that were collected from a few specific areas of different bridges, and there are very limited data sets that provide complete coverage of the entire bridge, making a detailed comparison of different computer vision methods difficult. This paper presents a benchmark data set that provides a series of annotations and field measurements required for traffic load detection, tracking, and continuous monitoring on the bridge. The data set was collected by five cameras and two weigh-in-motion systems installed on a cable-stayed bridge and is divided into three subsets. The first subset contains over 32,000 images and annotation files of 11 types of vehicle-related targets, which are necessary for the training of vehicle detection models. The second subset consists of photos of the calibration board and coordinates of reference points that are used for camera calibration. The last subset is designated for the field verification of various algorithms, providing synchronized vehicle weight data and monitoring videos covering the whole bridge. To the author's knowledge, this data set is the first open-source data set for vision-based traffic load monitoring in a bridge, which will have tremendous value in promoting research in the area of innovative bridge health monitoring technologies. Details of this data set will be available in the public domain through a Zenodo data repository.