Garbage Detection using YOLO Algorithm for Urban Management in Bangkok

Abstract

Garbage problems in urban areas are becoming more serious as the population increases, resulting in community garbage, including Bangkok, the capital of Thailand, being affected by pollution from rotten waste. Therefore, this research aims to apply deep learning technology to detect images from CCTV cameras in urban areas of Bangkok by using YOLO to detect images from CCTV cameras in urban areas of Bangkok, using YOLO to detect 1,383 images of overflowing garbage bins, classified into 2 classes: garbage class and bin class. YOLO in each version was compared, consisting of YOLOv5n, YOLOv6n, YOLOv7, and YOLOv8n. The comparison results showed that YOLOv5n was able to classify classes with an accuracy of 94.50%, followed by YOLOv8n at 93.80%, YOLOv6n at 71.60%, and YOLOv7 at 24.60%, respectively. The results from this research can be applied to develop a mobile or web application to notify of overflowing garbage bins by integrating with CCTV cameras installed in communities to monitor garbage that is overflowing or outside the bin and notify relevant agencies or the locals. This will allow for faster and more efficient waste management.