Recognition Method of Abnormal Behavior of Marine Fish Swarm Based on In-Depth Learning Network Model

Authors

  • Liyong Chen School of Network Engineering, Zhoukou Normal University, Zhoukou 466001, China
  • Xiuye Yin School of Computer Science and Technology, Zhoukou Normal University, Zhoukou 466001, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2031

Keywords:

In-depth learning network model, marine fish swarm, recognition, abnormal behavior

Abstract

In order to solve the problem that individual coordinates are easily ignored in the localization of abnormal behavior of marine fish, resulting in low recognition accuracy, execution efficiency and high false alarm rate, this paper proposes a method of fish abnormal behavior recognition based on deep learning network model. Firstly, the shadow of the fish behavior data is removed, and the background image is subtracted from each frame image to get the gray image of the fish school. Then, the label watershed algorithm is used to identify the fish, and the coordinates of different individuals in the fish swarm are obtained. Combined with the experimental size constraints and the number of fish, and combined with the deep learning network model, the weak link of video tag monitoring of abnormal behavior of marine fish is analyzed. Finally, the multi instance learning method and dual flow network model are used to identify the anomaly of marine fish school. The experimental results show that the method has high recognition accuracy, low false alarm rate and high execution efficiency. This method can provide a practical reference for the related research in this field.

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Author Biographies

Liyong Chen, School of Network Engineering, Zhoukou Normal University, Zhoukou 466001, China

Liyong Chen was born in 1982, male, Chinese, He received master’s degree in School of Computer Science and Technology, Faculty of Electronic Information, Liaoning University of Science and Technology, China, in 2010. He has been teaching at Zhoukou Normal University since 2010. His research interests include artificial intelligence and data mining.

Xiuye Yin, School of Computer Science and Technology, Zhoukou Normal University, Zhoukou 466001, China

Xiuye Yin was born in Xinyang, Henan. P.R. China, in 1984. He received the Master degree from university of science and technology Liaoning, P.R. China. His research interest include computational intelligence, Cloud computing and big data.

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Published

2021-05-05

Issue

Section

Articles