Safe Tracker - Combines Road Safety and Tracking Capabilities using Computer Vision

  • Unique Paper ID: 173569
  • Volume: 11
  • Issue: 10
  • PageNo: 958-964
  • Abstract:
  • In urban areas, traffic management and road safety are crucial issues. The objective of this project is to create a system that leverages AI and computer vision (specifically YOLO) alongside IoT-based sensors to identify road hazards and uphold traffic regulations. The system identifies and classifies potholes by estimating their depth, detects waterlogging, and evaluates the risk of electric current leakage through electrode-based sensing. Moreover, it tracks the use of high beam lights in areas that are already illuminated, employing an LDR sensor for this purpose. In cases of infringement, it produces challans and sends them via email. In order to assess road damage, the system categorizes potholes by severity and adjusts traffic routes accordingly, utilizing the Google Maps API. The system improves road safety by detecting vehicles that violate red signals and ensuring compliance, through the integration of traffic signal analysis. A Raspberry Pi equipped with a camera module is used for real-time video analysis, while an Arduino Nano is employed for sensor-based detections. With this solution, authorities can efficiently and economically keep an eye on road conditions and traffic offenses, thereby lowering the incidence of accidents and damage to infrastructure. By combining AI-based visual methods with IoT sensors, a dependable and scalable method for managing urban traffic is guaranteed. The suggested system aids in enhancing road safety, optimizing traffic flow, and facilitating efficient law enforcement.

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 10
  • PageNo: 958-964

Safe Tracker - Combines Road Safety and Tracking Capabilities using Computer Vision

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