Intelligent Traffic Monitoring And Accident Detection System Using YOLOv11 And Image Processing

Abstract

This paper presented improved road safety and traffic management by addressing delays in authorities’ response times through an intelligent traffic monitoring and accident detection system. Traditional traffic monitoring techniques frequently fall short of providing fast and accurate data required for immediate action due to the rising frequency of traffic congestion and vehicle accidents. To overcome these difficulties, the system made use of YOLOv11 to identify and track vehicles in traffic as well as image processing techniques to make real-time detection of road accidents. PyQt5 was used to create the stand-alone desktop application, which has an intuitive user interface and guarantees flawless operation even when offline. The system improved traffic officials’ capacity to react swiftly to accidents by giving them real-time data on vehicle movement and road accidents. The evaluation of the proposed algorithm showed comparable performance to existing state-of-the-art proving its effectiveness and reliability in traffic monitoring and accident detection with less compute. The result of this study has wide implications for smart city applications, local governance, and community welfare.