YOLO-Based Waste Detection in Smart Waste Management for a Cleaner Future: A Review

Automated waste detection is essential for effective urban waste management. In this context, several recent studies based on machine learning have demonstrated strong potential in the classification and identification of waste. In this work, we present an in-depth study highlighting the evolution of YOLO (You Only Look Once) object detection models, from YOLOv5 to YOLOv12, applied to real-time waste classification. YOLO's speed, accuracy, and lightweight architecture make it an ideal solution for edge computing deployment. Evaluations conducted on the IURWDD (Indian Urban and Rural Waste Detection Dataset), which consists of annotated images representing various types of urban and rural waste, show that compact variants such as YOLOv11n and YOLOv12n achieve excellent accuracy with minimal latency. Through this study, we identified several major challenges, including class imbalance and domain variability, and proposed strategies such as transfer learning and lightweight optimization. Overall, YOLO-based models show strong potential for developing scalable, sustainable, and real-time waste management solutions that meet the demands of smart cities.