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This paper explores the application of the You Only Look Once (YOLO) v11 model for real-time object detection in Indian road conditions, addressing challenges posed by unconventional objects like animals, autorickshaws, carts, and tractors. A dataset from dashcam and mobile footage was annotated using the Computer Vision Annotation Tool (CVAT) tool and combined with COCO to train YOLO v11. The model significantly improved detection accuracy, increasing classes from 30 to 108. Its high accuracy and real-time performance make it suitable for autonomous vehicles and traffic monitoring in India.

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Application of YOLO in Indian Driving Conditions

  • 会议论文

  • 发表日期：

    2026年

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  • S. G. Mohan,
  • Abhilash K. Raj,
  • S. A. Nayana,
  • S. Pradhaan,
  • Rajendra Bhat

摘要

This paper explores the application of the You Only Look Once (YOLO) v11 model for real-time object detection in Indian road conditions, addressing challenges posed by unconventional objects like animals, autorickshaws, carts, and tractors. A dataset from dashcam and mobile footage was annotated using the Computer Vision Annotation Tool (CVAT) tool and combined with COCO to train YOLO v11. The model significantly improved detection accuracy, increasing classes from 30 to 108. Its high accuracy and real-time performance make it suitable for autonomous vehicles and traffic monitoring in India.