The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Use of unmanned aerial vehicles for efficient beach litter monitoring
Open accessAbstract
A global beach litter assessment is challenged by use of low-efficiency methodologies and incomparable protocols that impede data integration and acquisition at a national scale. The implementation of an objective, reproducible and efficient approach is therefore required. Here we show the application of a remote sensing based methodology using a test beach located on the Saudi Arabian Red Sea coastline. Litter was recorded via image acquisition from an Unmanned Aerial Vehicle, while an automatic processing of the high volume of imagery was developed through machine learning, employed for debris detection and classification in three categories. Application of the method resulted in an almost 40 times faster beach coverage when compared to a standard visual-census approach. While the machine learning tool faced some challenges in correctly detecting objects of interest, first classification results are promising and motivate efforts to further develop the technique and implement it at much larger scales.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Accumulation and fragmentation of plastic debris in global environments
Barnes, D. K. A., Galgani, F., Thompson, R. C., & Barlaz, M. (2009) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2008.0205 · Cited by 6,126
Plastic debris in the open ocean
Cózar, A., Echevarría, F., González-Gordillo, J. I., Irigoien, X., Úbeda, B., Hernández-León, S., Palma, Á. T., Navarro, S., García-de-Lomas, J., Ruiz, A., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314705111 · Cited by 3,133
Self-citationInteractions of microplastic debris throughout the marine ecosystem
Galloway, T. S., Cole, M., & Lewis, C. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-017-0116 · Cited by 1,955
An up-to-date comparison of state-of-the-art classification algorithms
Zhang, C., Liu, C., Zhang, X., & Almpanidis, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.003 · Cited by 440
Self-citationUNEP/IOC Guidelines on Survey and Monitoring of Marine Litter.
Cheshire, A. C., Adler, E., Barbière, J., Cohen, Y., Evans, S., Jarayabhand, S., Jeftic, L., Jung, R. T., Kinsey, S., Kusui, E. T., et al. (2009) · United Nations Environment Programme · doi:10.25607/obp-726 · Cited by 346
Cited by
The physical oceanography of the transport of floating marine debris
van Sebille, E., Aliani, S., Law, K. L., Maximenko, N., Alsina, J. M., Bagaev, A., Bergmann, M., Chapron, B., Chubarenko, I., Cózar, A., et al. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/ab6d7d · Cited by 982
Drones for Conservation in Protected Areas: Present and Future
Jiménez López, J., & Mulero-Pázmány, M. (2019) · Drones · doi:10.3390/drones3010010 · Cited by 300
UAV remote sensing applications in marine monitoring: Knowledge visualization and review
Yang, Z., Yu, X., Dedman, S., Rosso, M., Zhu, J., Yang, J., Xia, Y., Tian, Y., Zhang, G., & Wang, J. (2022) · Science of The Total Environment · doi:10.1016/j.scitotenv.2022.155939 · Cited by 283
Papakonstantinou, A., Batsaris, M., Spondylidis, S., & Topouzelis, K. (2021) · Drones · doi:10.3390/drones5010006 · Cited by 99
Metadata
- Authors
- Martin, C., Parkes, S., Zhang, Q., Zhang, X., McCabe, M. F., & Duarte, C. M.
- Venue
- Marine Pollution Bulletin
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 131
- Issue
- Pt A
- Pages
- 662-673
- ISSN
- 0025-326X (ISSN-L)
- OpenAlex
- W2802213413
- Cited by
- 203
- References
- 7
- FWCI
- 6.39
- Type
- journal article
- Language
- en
- Open access
- green
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