Does drifting passage need to be linked to fish habitat assessment? Assessing environmental flow for multiple fish species with different spawning patterns with a framework integrating habitat connectivity

Zhang, Peng; Li, Qingyuan; Wang, Yuanming; Li, Kefeng; Qin, Leilei; Liang, Ruifeng

doi:10.1016/j.jhydrol.2022.128247

Cited by 13 publications

(1 citation statement)

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“…It enables fisheries managers to set appropriate catch limits, implement species-specific regulations, and ensure the conservation of economically important fish species. In aquaculture, classification helps in selecting suitable fish species for cultivation, understanding their nutritional requirements, and developing breeding programs to enhance productivity [3].…”

Section: Introductionmentioning

confidence: 99%

Attention ConvMixer Model and Application for Fish Species Classification

Le,

et al. 2023

EAI Endorsed Trans Ind Net Intel Syst

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Exploring the ocean has always been one of the foremost challenges for humankind, and fish classification is one of the crucial tasks in this endeavor. Manual fish classification methods, although accurate, consume significant time, money, and effort, while computer-based methods such as image processing and traditional machine learning often fall short of achieving high accuracy. Recently, deep convolutional neural networks have demonstrated their capability to ensure both time efficiency and accuracy in this task. However, deep convolutional networks typically have a large number of parameters, requiring substantial training time, and the convolutional operations lack attentional mechanisms. Therefore, in this paper, we propose the AttentionConvMixer neural network with Priority Channel Attention (PCA) and Priority Spatial Attention (PSA). The proposed approach exhibits good performance across all three fish classification datasets without introducing any additional parameters, thus demonstrating the effectiveness of our proposed method.

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Section: Introductionmentioning

confidence: 99%