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

Lawryan Andrew Darisang, Samuel Krishna Dwisetio, Ivan Sebastian Edbert, Alvina Aulia

2025

Abstract

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.

Paper Citation

in Harvard Style

Darisang L., Dwisetio S., Edbert I. and Aulia A. (2025). Real-Time Waste Detection Using YOLO, SSD, and Faster R-CNN Integrated with CNN-Based Classification. In Proceedings of the 1st International Conference on Research and Innovations in Information and Engineering Technology - Volume 1: RITECH; ISBN 978-989-758-784-9, SciTePress, pages 76-83. DOI: 10.5220/0014276000004928

in Bibtex Style

@conference{ritech25,
author={Lawryan Andrew Darisang and Samuel Krishna Dwisetio and Ivan Sebastian Edbert and Alvina Aulia},
title={Real-Time Waste Detection Using YOLO, SSD, and Faster R-CNN Integrated with CNN-Based Classification},
booktitle={Proceedings of the 1st International Conference on Research and Innovations in Information and Engineering Technology - Volume 1: RITECH},
year={2025},
pages={76-83},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014276000004928},
isbn={978-989-758-784-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Innovations in Information and Engineering Technology - Volume 1: RITECH
TI - Real-Time Waste Detection Using YOLO, SSD, and Faster R-CNN Integrated with CNN-Based Classification
SN - 978-989-758-784-9
AU - Darisang L.
AU - Dwisetio S.
AU - Edbert I.
AU - Aulia A.
PY - 2025
SP - 76
EP - 83
DO - 10.5220/0014276000004928
PB - SciTePress