Affiliations: [a] School of Computer Engineering, Jiangsu Ocean University, Lianyungang, Jiangsu, China | [b] Tai Zhou Polytechnic College, Taizhou, Jiangsu, China
Correspondence:
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Corresponding authors: Yanyan Chen and Hui Li, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, Jiangsu, China. E-mails: [email protected]; [email protected].
Abstract: There are many kinds of Marine organisms and their biological forms differ greatly, so it is difficult to guarantee the accuracy of artificial species identification, which brings great challenges to the work of Marine species identification. In this paper, we propose a recognition method of Marine biological image classification using residual neural network, redefining convolution layer and using batch regularization to avoid gradient parameter disorder. The bottleneck layer is realized by the residual connection in the neural network, and the residual network ResNet50 is constructed by the transfer learning method. The classification training was conducted on 19 common Marine animal data sets, and the experimental results showed that the recognition accuracy of ResNet50 reached about 90%. Compared with the traditional convolutional neural network VGG19, the results showed that the recognition efficiency of ResNet50 was better, thus verifying the effectiveness of the Marine animal classification and recognition model proposed in this paper.
Keywords: ResNet50, marine creatures classification, PyTorch, deep learning
