Traffic Detection and Enhancing Traffic Safety: YOLO V8 Framework and OCR for Violation Detection Using Deep Learning Techniques

• Published in: 2024 International Conference on Science Technology Engineering and Management (ICSTEM) pp. 1 - 6
• Main Authors: P, Nagaraj, K, Muthamil Sudar, Gurusigaamani, A. M., Vasanth, B Sai, Saranya, V S S, Reddy, P Girish Kumar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.04.2024
• Subjects: and optical character recognition; CNN; Deep learning; OCR; Open-cv; Optical character recognition; Pytesseract-OCR; PyTorch; Real-time systems; Road safety; Technological innovation; Transportation; YOLO; Yolo-V8
• DOI: 10.1109/ICSTEM61137.2024.10560904

Making sure traffic is safe and well-managed has become a top priority in the world of contemporary transportation. Using the robust YOLO (You Only Look Once) v8 model in conjunction with Optical Character Recognition (OCR) technology, this research explores the creation and deployment of a state-of-the-art traffic detection system. The main goal is to make roads safer by detecting traffic in real-time and automatically identifying violations. This research is built around the YOLO v8 framework, which is known for its fast and accurate object detection. With YOLO v8 and OCR technology integrated, the system's capabilities are greatly enhanced. Extracting textual information from licence plates allows the system to automatically detect violations like speeding. In order to make it work in a wide variety of international contexts, the OCR component is strong and can handle different fonts, sizes, and languages. To sum up, intelligent traffic management systems have come a long way using the YOLO v8 Framework and optical character recognition (OCR) for violation detection and traffic detection and enhancement.