Automated Vehicle Entry Monitoring System Using YOLOv5 - Dr. P. V. S. L. Jagadamba; Sai Suvarna Yandapalli; Basheerunnisa Shareef; Nyasa Harshitha Routhu; Sravya Nallajarla (International Journal of Electrical Machine Analysis and Design, 2024)
This project showcases an innovative You Only Look Once (YOLO) object detection model-based Automated Vehicle Entry Monitoring System for community gates. By using YOLO, the system transforms conventional access control paradigms by accurately and in real-time detecting vehicles that are seeking to gain entry. Unlike traditional approaches, the project leverages YOLO's effectiveness in vehicle recognition, classification and Number Plate Detection to improve residential security. By providing communities with a cutting-edge tool for gate access management, this project advances the field of intelligent security solutions as urban living dynamics change. The deployment of YOLO offers a state-of-the-art framework for effectively fortifying community gates, meeting contemporary security needs in a constantly shifting urban environment. The emergence of computerised systems for vehicle entry surveillance has been made possible by the quick expansion of urban infrastructure and the need for intelligent security solutions. The use of the YOLOv5 (You Only Look Once version 5) deep learning model to the development of dependable and effective car entrance monitoring systems is examined in this study. Because of its well-known real-time object identification features, YOLOv5 is a good option for these kinds of applications. The architecture, training techniques, performance indicators, and difficulties related to YOLOv5-based car monitoring systems are covered in this study.
Keywords: Darknet; OpenCV; YOLOv5; easy OCR; image preprocessing; number plate detection; object detection; tesseract