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This research addresses a critical knowledge gap in intelligent traffic management, here, E-Challan systems, by pioneering the exploration of real-time tow truck detection using comparative analysis of YOLOv8 and YOLOv9 models. Tow truck detection, an underexplored aspect, holds significant importance in scenarios such as accidents, breakdowns, and emergencies. Leveraging the advanced features of YOLOv8 and YOLOv9, including anchor-free detection and task-aligned assignment, our study presents a comprehensive framework for accurate and adaptable tow truck detection. Through benchmarking YOLOv8n, YOLOv8s, YOLOv8l, and YOLOv9c models, we perform a comparative analysis of their contributions and trade-offs, revealing the strengths of each version. Visualization graphs and evaluation metrics, including mAP-50 and mAP50-95, provide detailed insights into the performance comparison between YOLOv8 and YOLOv9, showcasing the superiority and improvement of the latest YOLOv9 model. This research fills a crucial gap in E-Challan systems and introduces an innovative approach to enhance traffic management and rule enforcement standards.

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Real-Time Tow Truck Detection: Filling the E-Challan Knowledge Gap Using YOLOv8 and YOLOv9

  • 会议论文

  • 发表日期：

    2025年

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  • Jaitej Singh,
  • Dharmendrasinh Rathod,
  • Parth Shah,
  • Priteshkumar Prajapati

摘要

This research addresses a critical knowledge gap in intelligent traffic management, here, E-Challan systems, by pioneering the exploration of real-time tow truck detection using comparative analysis of YOLOv8 and YOLOv9 models. Tow truck detection, an underexplored aspect, holds significant importance in scenarios such as accidents, breakdowns, and emergencies. Leveraging the advanced features of YOLOv8 and YOLOv9, including anchor-free detection and task-aligned assignment, our study presents a comprehensive framework for accurate and adaptable tow truck detection. Through benchmarking YOLOv8n, YOLOv8s, YOLOv8l, and YOLOv9c models, we perform a comparative analysis of their contributions and trade-offs, revealing the strengths of each version. Visualization graphs and evaluation metrics, including mAP-50 and mAP50-95, provide detailed insights into the performance comparison between YOLOv8 and YOLOv9, showcasing the superiority and improvement of the latest YOLOv9 model. This research fills a crucial gap in E-Challan systems and introduces an innovative approach to enhance traffic management and rule enforcement standards.