Adaptive vehicle counting method using YOLOv10 integrated with BoostTrack at symmetrical traffic light intersections
Atha Irrahman, Mirshal Arief, Arrad Iskandar, Khairun Saddami, Afdhal Afdhal, S Sugiarto, Nasaruddin Nasaruddin · Franklin Open · 2026
ABSTRACT The increasing volume of vehicles in urban areas creates complex traffic patterns, particularly at symmetrical intersections with mixed-traffic characteristics common in Indonesia. Accurate vehicle counting is essential for adaptive traffic signal systems, as real-time traffic density estimation can reduce waiting times, lower fuel consumption, and improve intersection efficiency. However, low illumination, inter-vehicle occlusion, and high traffic density remain major challenges for existing vehicle counting approaches. This study aims to develop a robust and efficient vehicle counting system for symmetrical intersections under mixed-traffic conditions. We introduce the Crossjunction Traffic Dataset (CJT-D), a novel annotated benchmark consisting of 5,000 images with 111,531 object annotations collected under four illumination conditions: morning, noon, afternoon, and night. Furthermore, we propose a unified framework integrating YOLOv10n as the primary detector with the confidence-aware BoostTrack tracker and an adaptive zone-based counting mechanism tailored to symmetrical intersection geometry. The detection model trained on CJT-D achieves a mean average precision of 0.952, an F1-score of 0.91, and an inference speed of 1.7 milliseconds per image. The tracking module achieves a higher-order tracking accuracy of 56.82, multiple objects tracking accuracy of 58.49, and identity consistency score of 72.70. Vehicle counting accuracy reaches 100% in the morning, 84–91% in the afternoon and evening, and 66–73% at night. These results confirm that the proposed framework delivers accurate, efficient, and occlusion-tolerant real-time vehicle counting at symmetrical intersections, offering strong potential for a computer vision-based adaptive traffic light control system.