Real-Time Waste Detection Using YOLO, SSD, and Faster R-CNN Integrated with CNN-Based Classification

: Substantial amount of unmanaged waste causes serious health and environmental risks, particularly in Indonesia, where 13.4 million tons were left unmanaged in 2024. However manual waste sorting is inefficient, labor-intensive, and prone to error, creating an urgent need for automated waste classification systems. This study proposes a real-time waste classification approach by integrating object detection models and a Convolutional Neural Network (CNN) classifier with the help of camera vision, through the transfer learning method. Object detection models YOLOv11, SSD-MobileNetV3, and Faster R-CNN with ResNet50 FPN were trained on TACO and Trash-ICRA19 datasets, while the CNN classifier with MobileNetV2-based was trained on the Domestic Waste Classification dataset. The MobileNetV2 classifier achieved 85.02% accuracy with a macro F1-score of 85%. For object detection models, YOLOv11 shows superior performance achieving mean Average Precision @.5:.95 of 55.69% with an inference speed of 14.1ms and 71.10 frames per second, outperforming others. The results indicate that YOLOv11 combined with CNN offers an efficient and accurate solution for real-time waste classification and scalable waste management applications.