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Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X. and Wei, X. (2022) YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv: 2209.02976.
https://arxiv.org/abs/2209.02976

has been cited by the following article:

• 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.