Open Access
Issue
E3S Web Conf.
Volume 664, 2025
4th International Seminar of Science and Applied Technology: “Green Technology and AI-Driven Innovations in Sustainability Development and Environmental Conservation” (ISSAT 2025)
Article Number 01003
Number of page(s) 9
Section Artificial Intelligence and Human-Computer Interaction
DOI https://doi.org/10.1051/e3sconf/202566401003
Published online 20 November 2025
  1. S. Sasaki, K. Watanabe, N. Widyaningsih, T. Araki, Collecting and dealing of recyclables in a final disposal site and surrounding slum residence: the case of Bantar Gebang, Indonesia. J. Mater. Cycles Waste Manag. 21, 375–393 (2019). https://doi.org/10.1007/s10163-018-0798-2 [Google Scholar]
  2. N. Ferronato, V. Torretta, Waste mismanagement in developing countries: a review of global issues, Int. J. Environ. Res. Public Health. 16, (2019). https://doi.org/10.3390/ijerph16061060 [Google Scholar]
  3. R. Fidelis, E.D.R. Guerreiro, D.J. Horst, G.M. Ramos, B.R. de Oliveira, P.P.A. Junior, Municipal solid waste management with recyclable potential in developing countries: current scenario and future perspectives, Waste Manag. Res. 41, 1399–1419 (2023). https://doi.org/10.1177/0734242X231160084 [Google Scholar]
  4. J.F. Palacios Sánchez, S. Vitery Gómez, L.F. Giraldo, Deep learning-based garbage bags and potholes detection model using Raspberry Pi. (2021) [Google Scholar]
  5. M. Tharani, A.W. Amin, M. Maaz, M. Taj, Attention neural network for trash detection on water channels. (2020) [Google Scholar]
  6. A. Gauli, S. Adhikar, B. Bhandari, B. Thapa, YOLO based abandoned garbage detection from video stream. 8914, 282–287 (2023) [Google Scholar]
  7. E.B. Setyawan, N. Novitasari, A.D. Zahira, Development of automatic object detection and IoT for garbage pickup assignment problem, Int. J. Informatics Vis. 8, 794–802 (2024). https://doi.org/10.62527/joiv.8.2.2740 [Google Scholar]
  8. T. Gelar, S. Fitriani, S. Rachmat, A systematic literature review of YOLO and IoT applications in smart waste management. 5, 123–139 (2025). https://doi.org/10.53623/gisa.v5i2.706 [Google Scholar]
  9. B. Okyere, L.A.O. Agyekum, An efficient computer vision and machine learning model for real-time litter detection on Raspberry Pi. 1, 10–24 (2023) [Google Scholar]
  10. A.B. Wahyutama, M. Hwang, YOLO-based object detection for separate collection of recyclables and capacity monitoring of trash bins. Electronics. 11, 1323 (2022). https://doi.org/10.3390/electronics11091323 [Google Scholar]
  11. T.K. Tran, K.T. Huynh, D.-N. Le, M. Arif, H.M. Dinh, A deep trash classification model on Raspberry Pi 4, Intell. Autom. Soft Comput. 35, 2479–2491 (2023). https://doi.org/10.32604/iasc.2023.029078 [Google Scholar]
  12. P. Li, J. Xu, S. Liu, Solid waste detection using enhanced YOLOv8 lightweight convolutional neural networks, Mathematics. 12, (2024). https://doi.org/10.3390/math12142185 [Google Scholar]
  13. S. Hon, S. Bhide, D. Jape, K. More, T. Nagare, M.B. Gawali, Intelligent model for smart waste detection and segmentation using YOLO v8. In: 2024 Int. Conf. Intell. Innov. Pract. Eng. Manag. (IIPEM). pp. 1–5. IEEE (2024) [Google Scholar]
  14. A. Das, J. Sayma, A. Nath, K.M.A. Hasan, Application of YOLOv11 classification for efficient waste segmentation in Australia’s recycling facilities. In: 2024 IEEE Asia- Pacific Conf. Geosci. Electron. Remote Sens. Technol. (AGERS). pp. 70–74. IEEE (2024) [Google Scholar]
  15. M.F.J. Permana, J.C.R.B. Gani, N.A. Fauzan, A. Adiwilaga, YOLOv11 model as a smart solution for waste identification and classification in automated waste management system. 2, 207–216 (2025). https://doi.org/10.21609/jiki.v18i2.1490 [Google Scholar]
  16. S.M. Itikap, M.S. Abdurrahman, E.B. Soewono, T. Gelar, Geometry and color transformation data augmentation for YOLOv8 in beverage waste detection, J. Softw. Eng. Inf. Commun. Technol. 4, 123–138 (2023). https://doi.org/10.17509/seict.v4i2.64400 [Google Scholar]
  17. C.H. Kang, S.Y. Kim, Real-time object detection and segmentation technology: an analysis of the YOLO algorithm, JMST Adv. 5, 69–76 (2023). https://doi.org/10.1007/s42791-023-00049-7 [Google Scholar]
  18. J.F. Lima, A. Patiño-León, M. Orellana, J.L. Zambrano-Martinez, Evaluating the impact of membership functions and defuzzification methods in a fuzzy system: case of air quality levels, Appl. Sci. 15, 1934 (2025). https://doi.org/10.3390/app15041934 [Google Scholar]
  19. H. Haviluddin, H.S. Pakpahan, N. Puspitasari, G.M. Putra, R.Y. Hasnida, R. Alfred, Adaptive neuro-fuzzy inference system for waste prediction. Knowl. Eng. Data Sci. 5, 122 (2022). https://doi.org/10.17977/um018v5i22022p122-128 [Google Scholar]
  20. R. Ghnemat, A. Shaout, Measuring waste recyclability level using convolutional neural network and fuzzy inference system, Int. J. Intell. Inf. Technol. 18, 1–17 (2022). https://doi.org/10.4018/IJIIT.306969 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.