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Research on sheep face recognition algorithm based on improved AlexNet model

  • S.I.: Evolutionary Computation-based Methods and Applications for Data Processing
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Abstract

To better solve the problems of the intensive and large-scale sheep farm, such as difficult basic data collection and individual performance discrimination, this paper proposes a sheep face recognition algorithm based on the improved AlexNet Model. Based on AlexNet Model, the receptive field size is increased, the local response normalization is canceled, the FC1 layer is replaced with the Senet module of attention mechanism, and the Relu activation function is replaced with the mish function. We apply it to sheep face recognition. At Gansu Zhongtian sheep farm, sheep face data were collected, and a sheep face dataset was constructed. Through the test, the recognition accuracy is about 98.37%, and the recognition accuracy of the sheep face verification set tracked and collected after 100 days is about 96.58%, which proves that the improved AlexNet network model can quickly and accurately identify sheep individuals. Therefore, the model provides a new idea for the research of sheep face recognition and has specific application and popularization value.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Correspondence to Fang Tian.

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Zhang, C., Zhang, H., Tian, F. et al. Research on sheep face recognition algorithm based on improved AlexNet model. Neural Comput & Applic 35, 24971–24979 (2023). https://doi.org/10.1007/s00521-023-08413-3

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