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A gender classification method for Chinese mitten crab using deep convolutional neural network

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Abstract

Chinese mitten crab is a very popular food in China. Due to different nutrients between the male crab and the female crab, it is necessary to distinguish crab’s gender before selling in the market. Current computer vision methods need complex image preprocessing and cannot distinguish crab’s gender through the crab shell. In this paper, a new classification approach for crab’s gender using the deep convolutional neural network (CNN) was proposed. Firstly, four different types of data augmentation methods were used to enrich the data set. Then, a batch normalization technique was taken to solve the problem of the gradient disappearance and shorten the training time. In order to suppress over-fitting phenomenon, a dropout technique was also taken into consideration. Experimental results demonstrated the effectiveness of the proposed crab’s gender classification method. The proposed method achieved 98.90% classified accuracy which is 3 percentage points higher than two state-of-the-art methods.

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Acknowledgments

This study was supported by the National Natural Science Foundation of China under Grant 61873113 & the Key R&D Program of Jiangsu Province, China (Grant number BE2018370).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering & Automation, Anhui University, Hefei, 230601, Anhui, China

    Yanhai Cui, Tianhong Pan & Shan Chen

  2. School of Electrical & Information Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China

    Yanhai Cui & Tianhong Pan

  3. School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China

    Xiaobo Zou

Authors
  1. Yanhai Cui
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  2. Tianhong Pan
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  3. Shan Chen
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  4. Xiaobo Zou
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Correspondence to Tianhong Pan.

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Cui, Y., Pan, T., Chen, S. et al. A gender classification method for Chinese mitten crab using deep convolutional neural network. Multimed Tools Appl 79, 7669–7684 (2020). https://doi.org/10.1007/s11042-019-08355-w

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  • DOI: https://doi.org/10.1007/s11042-019-08355-w

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