Automated Vehicle Access Control System Utilizing Computer Vision-Based License Plate Recognition

Abstract

The evaluation of the Automated Vehicle Access Control System utilizing Computer Vision-Based License Plate Recognition demonstrated excellent performance based on ISO/IEC 25010:2011 quality metrics, achieving a Grand Mean of 3.31, categorized as "Excellent." Individual metrics such as Maintainability (3.38), Portability (3.40), and Reliability (3.35) ranked highest, reflecting the system's robust quality. The system’s functionality and performance were also rated "Highly Accepted" by respondents, with a Grand Mean of 3.47. Readiness evaluations indicated the infrastructure and personnel were "Very Ready" to support implementation, with a Grand Mean of 3.47. These findings align with related studies emphasizing the efficiency of image-based entry management systems employing vehicle and facial recognition technologies, which enhance security, automate access control, and reduce manual workload. Leveraging advanced techniques like CNN and OpenCV, these systems prove effective in organizational settings, providing real-time monitoring, attendance tracking, and vehicle management capabilities. The high accuracy and readiness demonstrated by the system affirm its reliability and effectiveness for deployment.