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A Design and Implementation of Real-time Automatic Traffic Violation Detection and Reporting System based on Edge Devices

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2025, 30(5), pp.1~8
  • DOI : 10.9708/jksci.2025.30.05.001
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : April 1, 2025
  • Accepted : May 5, 2025
  • Published : May 30, 2025

Minsoo Sun 1 Jeonghak Nam 2 Hyunseok Song 3 Eunseo Jang 4 Yoonji Jung 5 Sejong Lee 6 Ho-Young Kwak 1

1제주대학교
2세종대학교
3대구대학교
4강남대학교
5인하대학교 공간정보공학연구소
6영남대학교

Accredited

ABSTRACT

In this paper, we propose an edge device-based automated traffic law violation detection and easy reporting system. This system automatically detects traffic law violations such as signal violations, reckless driving, not wearing a motorcycle helmet, and vehicles driving with their tail lights off at night in a road traffic environment. It collects images in real time from edge devices and analyzes the images using a high-performance deep learning model (YOLOv8) and a lane detection model (UFLDv2) to detect various traffic law violations. In particular, to reduce false detection rates, vehicles driving without taillights are detected through multi-frame continuous detection, while helmet violations on two-wheeled vehicles are processed immediately in a single frame. Additionally, the system integrates vehicle license plate recognition and an automated reporting procedure, allowing for easy reporting of traffic law violations. The proposed system demonstrated an accuracy of 83.3% and a recall of 71.4% in detecting traffic violations. Future work will focus on validation in diverse environments and model optimization to enhance commercialization potential.

Citation status

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