Open Access
Issue
E3S Web Conf.
Volume 643, 2025
2025 7th International Conference on Environmental Sciences and Renewable Energy (ESRE 2025)
Article Number 01004
Number of page(s) 10
Section Environmental Pollution Monitoring and Waste Management
DOI https://doi.org/10.1051/e3sconf/202564301004
Published online 29 August 2025
  1. P. Fraternali, L. Morandini, S. L. H. González, “Solid waste detection, monitoring and mapping in remote sensing images: A survey”, Waste Management, vol. 189, no. 12, pp. 88–102 (2024) [Google Scholar]
  2. L. Zhou, X. Rao, Y. Li, X. Zuo, Y. Liu, Y. Lin, Y. Yang, “SWDet: Anchor-Based Object Detector for Solid Waste Detection in Aerial Images”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 306–320 (2023) [Google Scholar]
  3. R. N. Torres, P. Fraternali, “Learning to Identify Illegal Landfills through Scene Classification in Aerial Images”, Remote Sensing, vol. 13, p. 4520 (2021) [Google Scholar]
  4. W. Luo, W. Han, P. Fu, H. Wang, Y. Zhao, K. Liu, Y. Liu, Z. Zhao, M. Zhu, R. Xu, G. Wei, “A Water Surface Contaminants Monitoring Method Based on Airborne Depth Reasoning”, Processes, vol. 10, no. 1, p. 131 (2022) [Google Scholar]
  5. N. Maharjan, H. Miyazaki, B. M. Pati, M. N. Dailey, S. Shrestha, T. Nakamura, “Detection of River Plastic Using UAV Sensor Data and Deep Learning”, Remote Sensing, vol. 14, no. 13, p. 3049 (2022) [Google Scholar]
  6. I. Cortesi, A. Masiero, G. Tucci, K. Topouzelis, “UAV-based river plastic detection with a multispectral camera”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 43, pp. 855–861 (2022) [Google Scholar]
  7. M. Kraft, M. Piechocki, B. Ptak, K. Walas, “Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle”, Remote Sensing, vol. 13, no. 5, p. 965 (2021) [Google Scholar]
  8. T. Malche, P. Maheshwary, P. K. Tiwari, A. H. Alkhayyat, A. Bansal, R. Kumar, “Efficient solid waste inspection through drone-based aerial imagery and TinyML vision model”, Transactions on Emerging Telecommunications Technologies, vol. 35, no. 4 (2024) [Google Scholar]
  9. S. Gao, Y. Liu, S. Cao, Q. Chen, M. Du, D. Zhang, J. Jia, W. Zou, “IUNet-IF: identification of construction waste using unmanned aerial vehicle remote sensing and multi-layer deep learning methods”, International Journal of Remote Sensing, vol. 43, pp. 7181–7212 (2022) [Google Scholar]
  10. Y. Liu, B. Zhao, X. Zhang, W. Nie, P. Gou, J. Liao, K. Wang, “A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery”, Applied Sciences, vol. 14, no. 5, p. 2084 (2024) [Google Scholar]
  11. J. Wang, W. Guo, T. Pan, H. Yu, L. Duan, W. Yang, “Bottle detection in the wild using low-altitude unmanned aerial vehicles”, 21st International Conference on Information Fusion (FUSION), pp. 439–444 (2018) [Google Scholar]
  12. M. Yaseen, “What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector,” arXiv (2024) [Google Scholar]
  13. S. Ren, K. He, R. Girshick, J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 6, pp. 1137–1149 (2016) [Google Scholar]
  14. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, L. Zitnick, “Microsoft coco: Common objects in context”, Computer Vision ECCV 2014: 13th European Conference, Zurich (2014) [Google Scholar]
  15. K. He, X. Zhang, S. Ren, J. Sun, “Deep residual learning for image recognition”, IEEE conference on computer vision and pattern recognition, Las Vegas (2016) [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.