Intelligent Traffic Violation Detection and Notification System Using Deep Learning

Existing traffic monitoring systems face significant limitations in ensuring road safety and enforcing traffic laws. Traditional systems often rely on manual intervention for identifying violations such as speeding or illegal parking, leading to inefficiencies and delays. Many existing solutions use outdated object detection methods that fail to provide real time, high accuracy results, especially in high density or low light traffic scenarios. Additionally, these systems lack integration for automated notifications and incident reporting, making it difficult for authorities to respond promptly. The proposed intelligent vehicle tracking system addresses these limitations by integrating advanced artificial intelligence and computer vision technologies. Vehicle detection is performed using the YOLOv8 model, renowned for its high accuracy and real time processing capabilities, ensuring precise identification of vehicles in various conditions. Speed monitoring leverages OpenCV to calculate vehicle speeds based on timestamps and predefined distances, while license plate recognition is achieved using EasyOCR, which extracts text from plates and links it to an owner database. The system includes an incident reporting module, allowing users to upload accident photos, with geolocation APIs automatically providing the device’s location for immediate response by law enforcement. Comparative testing revealed that the proposed system achieved a 95% success rate in vehicle detection and 92% in number plate recognition, surpassing the 85% accuracy of traditional methods. Furthermore, speed monitoring in the proposed system demonstrated consistent reliability with a ±3 km/h error margin, compared to higher error rates in existing solutions. The automated notification and incident reporting features significantly improve response times, addressing a critical gap in conventional systems. This system represents a significant step forward in leveraging technology for smarter traffic management, offering scalability for deployment in both urban and rural areas. Future enhancements include integration with national vehicle registries and predictive analytics for accident prone zones.