Abstract
The good marine ecological environment is the basis for the sustainable development and utilization of marine resources. However, humans have also severely damaged the marine environment while utilizing marine resources. Therefore, image classification for marine pollution is beneficial to the protection and development of the ocean. In recent years, with the rise of convolution neural networks, this algorithm is rarely used in the classification of marine pollutants. This paper will apply the design of 6-layer convolution neural network to image classification of marine pollution (called for short MP-net). Experiments show that Alex net, VGG(11) and MP-net are learning and training in the same data set, and the accuracy rates respectively are 89.17%, 86.25%, and 90.14%. Therefore, in the image classification of marine pollutants using convolution neural networks, the network can adapt to image scenes, automatically learn features, and have good classification results.
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Acknowledgement
This work is supported by Hainan Provincial Natural Science Foundation of China (project number: 20166235), Hainan provincial university scientific research funding project (project number: Hnky2017-57).
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Yang, T., Jia, S., Zhang, H., Zhou, M. (2018). Research on Image Classification of Marine Pollutants with Convolution Neural Network. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11068. Springer, Cham. https://doi.org/10.1007/978-3-030-00021-9_59
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