International Research Journal of Education and Technology

The Dress Code Monitoring System using YOLOv4 aims to enhance compliance with dress code policies in educational institutions and workplaces through an innovative application of computer vision and deep learning technologies. This system leverages the capabilities of the YOLOv4 (You Only Look Once) algorithm, renowned for its real-time object detection and classification efficiency. By integrating high-resolution cameras and the YOLOv4 model, the system can accurately identify and analyze individuals’ attire as they enter designated areas. The primary objective is to ensure adherence to specified dress codes, which can range from formal to casual or specific uniforms. The system operates by continuously monitoring live video feeds, detecting individuals, and classifying their attire in real time. If a mismatch with the dress code is detected, the system can trigger alerts or notifications to relevant authorities, such as administrators or supervisors. Furthermore, the application is designed to maintain privacy by anonymizing detected individuals and ensuring that data storage complies with relevant regulations. This innovative approach not only fosters a sense of professionalism and unity within the institution but also streamlines the enforcement of dress code policies, reducing the need for manual monitoring. The Dress Code Monitoring System using YOLOv4 represents a significant advancement in leveraging artificial intelligence for practical applications, demonstrating the potential for technology to enhance operational efficiency and compliance in various settings.

Keywords: Computer Vision, Deep Learning, Object Detection, Real-Time