TITLE:
Traffic Object Detection Using YOLOv12
AUTHORS:
Qizhao Chen
KEYWORDS:
Object Detection, YOLOv12, Traffic Monitoring, Deep Learning, Computer Vision, Real-Time Detection, Bounding Box Localization
JOURNAL NAME:
Open Access Library Journal,
Vol.12 No.8,
August
14,
2025
ABSTRACT: Traffic monitoring plays a vital role in smart city infrastructure, road safety, and urban planning. Traditional detection systems, including earlier deep learning models, often struggle with balancing accuracy, speed, and generalization in diverse and dynamic environments. YOLOv12, the latest model in the YOLO series, introduces architectural improvements such as attention-based mechanisms and efficient layer aggregation, enabling it to overcome limitations related to small object detection, inference latency, and optimization stability. This study evaluates YOLOv12 using a globally sourced traffic dataset that includes varied weather conditions, lighting scenarios, and geographic locations. The model demonstrates strong performance across key object detection metrics, achieving high precision, recall, and mean Average Precision (mAP). Results indicate that YOLOv12 is highly effective for real-time traffic object detection and offers significant improvements over previous approaches, making it a robust solution for large-scale deployment in intelligent transportation systems.