DETECTION AND MONITORING OF BEACH LITTER USING UAV IMAGE AND DEEP NEURAL NETWORK

22 citations as recorded by crossref.

1. A pilot study to estimate the population size of endangered Galápagos marine iguanas using drones A. Varela-Jaramillo et al. https://doi.org/10.1186/s12983-022-00478-5
2. Monitoring and Quantifying the Fluvio-Geomorphological Changes in a Torrent Channel Using Images from Unmanned Aerial Vehicles G. Gkiatas et al. https://doi.org/10.3390/hydrology9100184
3. Citizen Science for Marine Litter Detection and Classification on Unmanned Aerial Vehicle Images S. Merlino et al. https://doi.org/10.3390/w13233349
4. A Study on the Monitoring of Floating Marine Macro-Litter Using a Multi-Spectral Sensor and Classification Based on Deep Learning Y. Jeong et al. https://doi.org/10.3390/rs16234347
5. Automatic Marine Debris Inspection Y. Liao & J. Juang https://doi.org/10.3390/aerospace10010084
6. An Intelligent Hierarchical Cyber-Physical System for Beach Waste Management: The BIOBLU Case Study G. Cicceri et al. https://doi.org/10.1109/ACCESS.2023.3317689
7. Real-Time UAV Trash Monitoring System Y. Liao & J. Juang https://doi.org/10.3390/app12041838
8. Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q) M. Wolf et al. https://doi.org/10.1088/1748-9326/abbd01
9. SRGO-YOLO: Super-Resolution and Granularity-Optimized Detection of Extremely Small Beach-Litter Objects in UAV Imagery C. Luo et al. https://doi.org/10.1109/JSTARS.2026.3696988
10. Implementing land-based litter surveys through visual inspection of imagery using unmanned aerial vehicles S. Swanepoel et al. https://doi.org/10.1016/j.envc.2023.100753
11. UxV-Based Deep-Learning-Integrated Automated and Secure Garbage Management Scheme Using Blockchain M. Masuduzzaman et al. https://doi.org/10.1109/JIOT.2022.3156617
12. A Drone-Based Multisensory Payload for Maritime Pollutants Detections E. Giusti et al. https://doi.org/10.1109/MAES.2022.3232071
13. Litter detection from aerial imagery: a review of UAV-based approaches and deep learning techniques M. Bartolo et al. https://doi.org/10.1007/s11042-026-21440-1
14. Drone technology in municipal solid waste management and landfilling: A comprehensive review N. Sliusar et al. https://doi.org/10.1016/j.wasman.2021.12.006
15. Quantifying Floating Plastic Debris at Sea Using Vessel-Based Optical Data and Artificial Intelligence R. de Vries et al. https://doi.org/10.3390/rs13173401
16. From coastal geomorphometry to virtual environments F. Gross et al. https://doi.org/10.3389/fmars.2023.1229489
17. A review of human-UAV interaction for safety: an ISM-MICMAC approach S. Pattepu et al. https://doi.org/10.1007/s11135-026-02583-7
18. Application of Direct and Indirect Methodologies for Beach Litter Detection in Coastal Environments A. Sozio et al. https://doi.org/10.3390/rs16193617
19. Enhancing Urban Understanding Through Fine-Grained Segmentation of Very-High-Resolution Aerial Imagery U. Raman Kumar et al. https://doi.org/10.3390/rs17101771
20. Macroplastic survey on a sea turtle nesting beach at the Pacific coast of Guatemala S. Reinhardt et al. https://doi.org/10.1016/j.rsma.2026.105226
21. Does plastic waste present an infection hazard? Current evidence, surveillance challenges and research priorities P. Kalonde et al. https://doi.org/10.1186/s42522-026-00237-0
22. UAV-Based Landfill Land Cover Mapping: Optimizing Data Acquisition and Open-Source Processing Protocols C. Wyard et al. https://doi.org/10.3390/drones6050123