Oil Identification on Beaches Using Deep Learning Techniques
Resumo
The oil spill on the beaches of the Brazilian northeast coast has caused disastrous consequences, both for us human beings, as well as for the marine biodiversity. Bearing in mind that its identification is not easy, we propose to do it in this article, using a machine learning approach, a type of artificial intelligence widely used in the area of image classification, alongside with a Convolutional Neural Network. In this regard, we have as main objective the training of a model able to distinguish images, automatically, in three types of classes: (i) normal beaches; (ii) beaches with gulf-weed, a biological indicator of pollution; and (iii) beaches with oil. In the best scenario evaluated, we achieved an average accuracy of 91%.Referências
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W. Zhang, S. Song, T. Bai, Y. Zhao, F. Ma, J. Su, and L. Yu, "Chromosome classification with convolutional neural network based deep learning," in 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP- BMEI), 2018, pp. 1–5.
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A. Correia, M. Simões-Marques, and R. Graça, Automatic Classification of Incidents in Coastal Zones, 07 2020, pp. 123–129.
M. Krestenitis, G. Orfanidis, K. Ioannidis, K. Avgerinakis, S. Vrochidis, and I. Kompatsiaris, "Oil spill identification from satellite images using deep neural networks," Remote Sensing, vol. 11, no. 15, p. 1762, Jul 2019. [Online]. Available: http://dx.doi.org/10.3390/rs11151762
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F. Arias Del Campo, O. O. Vergara, V. G. Cruz, L. A. García, and M. Nandayapa, "Influence of image pre-processing to improve the accuracy in a convolutional neural network," International Journal of Combinatorial Optimization Problems and Informatics, vol. 11, no. 1, pp. 88–96, Jan. 2020.
A. H. R. C. F. L. Silva, "A survey on transfer learning for multiagent reinforcement learning systems," Journal of Artificial Intelligence Re- search, vol. 64, pp. 645–703, 2019.
T. Akiba, S. Suzuki, and K. Fukuda, "Extremely large minibatch in 15 minutes," CoRR, vol. training resnet-50 on imagenet SGD: abs/1711.04325, 2017.
A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, "Deep learning for computer vision: A brief review," Computational Intelligence and Neuroscience, vol. 2018, p. 7068349, Feb 2018.
M. Antonelli Ponti and G. B. Paranhos da Costa, "Como funciona o Deep Learning," arXiv e-prints, p. arXiv:1806.07908, Jun. 2018.
S. Targ, D. Almeida, and K. Lyman, "Resnet in resnet: Generalizing residual architectures," arXiv preprint arXiv:1603.08029, 2016.
S. L. Lau, E. K. Chong, X. Yang, and X. Wang, "Automated pavement crack segmentation using u-net-based convolutional neural network," IEEE Access, vol. 8, pp. 114 892–114 899, 2020.
Publicado
07/11/2020
Como Citar
NEGREIROS, Ramoni Reus Barros; DOS SANTOS, Rafael Araújo; ALVES, André Luiz Firmino; FIRMINO, Anderson Almeida.
Oil Identification on Beaches Using Deep Learning Techniques. In: WORKSHOP DE TRABALHOS DA GRADUAÇÃO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 33. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
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p. 167-170.
DOI: https://doi.org/10.5753/sibgrapi.est.2020.13003.
