Implementation of Web-based Realtime Monitoring System Using YOLOv8 for Green Box Detection and Automatic Capture in Navigation Missions at the Indonesian Boat Contest

This research presents the design and development of a real-time object detection and monitoring system specifically aimed at identifying green box objects using the YOLOv8 model. The system integrates OpenCV for frame-by-frame video processing, MySQL for image storage as Binary Large Objects (BLOBs), and a Flask-based web interface for real-time visualization. Green box objects detected with a confidence score above 0.7 are cropped and stored in the database. A dynamic web interface, updated every 2 seconds using AJAX, enables real-time monitoring and allows users to download the latest detected image for further analysis. Experimental results demonstrate that the YOLOv8 model achieves high detection accuracy, as measured by precision, recall, and mean average precision (mAP). The proposed system effectively combines object detection, data storage, and web-based visualization to provide a robust and scalable solution for real-time monitoring. Tests conducted under real-world conditions confirm the system's efficiency and reliability. Future work may involve hardware acceleration via edge computing, support for multi-object detection, and integration of advanced tracking algorithms to broaden its applicability in autonomous systems and industrial automation.