A vision-based weigh-in-motion approach for vehicle load tracking and identification

With the rapid increase in the number of vehicles, accurately identifying vehicle loads is crucial for maintaining and operating transportation infrastructure systems. Existing load identification methods typically rely on collecting vehicle load data from weigh-in-motion (WIM) systems when vehicles pass over them. However, cumbersome installation, high costs, and regular maintenance are the main obstacles that prevent WIM from being widely used in practice. This study introduces the visual WIM (V-WIM) framework, a vision-based approach for tracking and identifying moving loads. The V-WIM framework consists of two main components, the vehicle weight estimation and the vehicle tracking and location estimation. Vehicle weight is estimated using tire deformation parameters extracted from tire images through object detection and optical character recognition techniques. A deep learning-based YOLOv8 algorithm is employed as a vehicle detector, combined with the ByteTrack algorithm for tracking vehicle location. The vehicle weight and its corresponding location are then integrated to enable simultaneous vehicle weight estimation and tracking. The performance of the proposed framework was evaluated through two component validation tests and one on-site validation test, demonstrating its capability to overcome the limitations of existing methods.