A lightweight end to end traffic congestion detection framework using HRTNet on the Qinghai Tibet plateau

Managing traffic in plateau regions, particularly on critical routes like the Qinghai-Tibet Line, presents significant challenges from extreme climate and terrain. Traditional monitoring methods struggle with image distortion and information loss in rain, compromising congestion detection. To address these issues, we propose HRTNet, a lightweight real-time end-to-end model for congestion monitoring. HRTNet uses a clever design to clear rain streaks and highlight key details, making it easier to spot vehicles in bad weather. We also introduce the RainyRoad-PlateauDataset (RRPD), the first of its kind, with 3750 images capturing high-altitude rainy roads. This dataset is tailored to test performance under tough meteorological and topographical conditions. Evaluations show HRTNet achieves an average precision of 46.8% on the COCO val2017 and 134 FPS on an NVIDIA A6000 GPU. On RRPD, its precision rises to 50.5%, beating RT-DETR-r18 by 9.2%. HRTNet was deployed to monitor traffic congestion on rainy plateaus, enabling accurate calculation of congestion duration and distance. This provides critical support for intelligent traffic management systems like dynamic congestion warning and adaptive signal control. Beyond plateaus, this work offers solutions for traffic systems in other harsh environments.