Real-Time Object Detection in Tap Water Utilizing YOLOv8 for Comprehensive Contamination Monitoring - Glajemir E. Bautista; Paula Angelyn D. Gargar; Ramon G. Garcia (Engineering Proceedings)
The study presents a real-time object detection system for tap water contamination, employing the YOLOv8 model to identify specific pollutants, including algae, ants, and sand. The presence of these pollutants poses significant risks to water quality and public health, particularly in urban settings. To address these concerns, a high-precision object detection system was implemented to monitor and analyze water samples effectively. The developed system integrates unconventional image processing, enabling the accurate identification of foreign objects and potential contaminants with high accuracy. The data collected were utilized to train the YOLOv8 model, ensuring reliable performance across diverse environmental conditions. The system provides timely detection of contaminants through real-time analysis, facilitating proactive water quality management. The performance of the YOLOv8 model was systematically evaluated using key metrics, including precision, recall, and inference speed, to validate its effectiveness. This object detection device represents a critical advancement in safeguarding public health by incorporating machine learning into existing water quality monitoring frameworks, ultimately supporting sustainable and safe urban water management.