A fish appetite assessment method based on improved ByteTrack and spatiotemporal graph convolutional network
文献类型： 外文期刊
作者： Zhao, Haixiang 1 ; Cui, Hongwu 2 ; Qu, Keming 2 ; Zhu, Jianxin 2 ; Li, Hao 2 ; Cui, Zhengguo 2 ; Wu, Yuankai 3 ;
作者机构： 1.Shanghai Ocean Univ, Coll Fisheries & Life Sci, Shanghai, Peoples R China
2.Yellow Sea Fisheries Res Inst, Chinese Acad Fishery Sci, Qingdao 266071, Peoples R China
3.Tech Univ Munich, Munich Inst Robot & Machine Intelligence MIRMI, Chair Media Technol, Sch Computat, D-80333 Munich, Germany
4.Laoshan Lab, Qingdao 266237, Peoples R China
关键词： Aquaculture; Fish appetite; Deep learning; Spatiotemporal graph convolutional neural; network
期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：5.1； 五年影响因子：5.5 ）
ISSN： 1537-5110
年卷期： 2024 年 240 卷
页码：
收录情况： SCI
摘要： The appetite of fish significantly influences aquaculture efficiency and fish welfare. However, accurately assessing fish appetite has posed a challenging problem. Currently, the study of fish feeding behaviour relies primarily on the overall information obtained from images of fish schools, often overlooking the distinctive behavioural traits of individual fish. Analysing the behaviour of individual fish is hindered by challenges such as intraclass variation and cross-occlusion within real aquaculture environments. To address these challenges, this paper introduces a novel method for assessing appetite based on individual fish behaviour. The ByteTrack model was improved to enable stable tracking of each fish within a school under complex conditions. Additionally, this paper employs the spatiotemporal graph convolutional neural network (ST-GCN) to extract the movement characteristics of individual fish, facilitating accurate appetite assessment. The experimental results demonstrate that the proposed method achieves 98.47% accuracy in appetite assessment, surpassing the performance of other state-of-the-art methods. This paper provides a new opportunity and effective means for analysing fish behaviour and appetite in intricate environments.
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