Real-Time Detection of Heavy-Duty Vehicle Lane Violations: A Comparative Analysis of Yolov11 and Yolov12
Author :
Abstract
The unnecessary occupation of the left lane on highways and main roads by heavy-duty vehicles (such as trucks and buses) poses a significant traffic safety problem. Due to their low speeds and large sizes, these vehicles disrupt traffic flow, forcing other drivers into sudden braking or lane-changing maneuvers, thereby increasing the risk of accidents. Studies conducted indicate that left-lane violations by commercial vehicles contribute to nearly 15% of traffic accident globally.
In this study, an AI-based prototype system was developed to detect and prevent left-lane violations by heavy-duty vehicles. The system employs a vehicle-mounted camera that analyzes lane markings, road barriersand surrounding vehicles in real time. The captured data were processed using two state-of-the-art object detection models, YOLOv11 and YOLOv12, and their performances were comparatively evaluated.
Experimental results demonstrate that YOLOv12 outperforms YOLOv11 in terms of overall performance, achieving higher values in both mAP@50 (85.6%) and precision (89%). YOLOv12 yielded superior detection results for the car (93.6%), bus (72.6%), and lane violation (96.6%) classes. However, for the truck class, YOLOv11 achieved slightly better accuracy (86.0%) compared to YOLOv12 (80.6%). Training curves further revealed that YOLOv12 stabilized its losses more rapidly and exhibited a more consistent learning process.
In conclusion, the proposed system provides real-time detection of left-lane violations and delivers visual and auditory warnings to drivers, thereby encouraging safer lane usage. The comparative analysis of YOLOv11 and YOLOv12 highlights that YOLOv12 generally offers superior performance, while class-specific variations underline the importance of model selection in traffic safety applications.
Keywords
Abstract
The unnecessary occupation of the left lane on highways and main roads by heavy-duty vehicles (such as trucks and buses) poses a significant traffic safety problem. Due to their low speeds and large sizes, these vehicles disrupt traffic flow, forcing other drivers into sudden braking or lane-changing maneuvers, thereby increasing the risk of accidents. Studies conducted indicate that left-lane violations by commercial vehicles contribute to nearly 15% of traffic accidents globally.
In this study, an AI-based prototype system was developed to detect and prevent left-lane violations by heavy-duty vehicles. The system employs a vehicle-mounted camera that analyzes lane markings, road barriersand surrounding vehicles in real time. The captured data were processed using two state-of-the-art object detection models, YOLOv11 and YOLOv12, and their performances were comparatively evaluated.
Experimental results demonstrate that YOLOv12 outperforms YOLOv11 in terms of overall performance, achieving higher values in both mAP@50 (85.6%) and precision (89%). YOLOv12 yielded superior detection results for the car (93.6%), bus (72.6%), and lane violation (96.6%) classes. However, for the truck class, YOLOv11 achieved slightly better accuracy (86.0%) compared to YOLOv12 (80.6%). Training curves further revealed that YOLOv12 stabilized its losses more rapidly and exhibited a more consistent learning process.
In conclusion, the proposed system provides real-time detection of left-lane violations and delivers visual and auditory warnings to drivers, thereby encouraging safer lane usage. The comparative analysis of YOLOv11 and YOLOv12 highlights that YOLOv12 generally offers superior performance, while class-specific variations underline the importance of model selection in traffic safety applications.
