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LFCNet: A lightweight fish counting model based on density map regression
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DOI：10.1016/j.compag.2022.107496.png)
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摘要
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Accurate counting of fish is a fundamental task of fish farming and crucial for density regulation, bait planning, and economic benefit evaluation. However, computational capability is a substantial concern for practical aquaculture applications. The paper proposes a lightweight model especially for fish counting applications, termed LFCNet. The model is divided into three components: encoder, decoder, and generation head. Firstly, density map regression is utilized to address the high-density fish issue, making adhesion state counting more accurate. Secondly, Ghost modules are embedded to compress the parameters in mobile device applications. Finally, in order to realize the high-precision counting in a lightweight network, three concentrated -comprehensive convolution (C3) modules and transposed convolution layers are adopted to alleviate the computational overhead and recover the resolution of feature maps, respectively. Experiments show that pa-rameters (16.26 MB) and floating point operations (FLOPs: 69.64G) of LFCNet are reduced by 73.8 % and 64.9 % compared with the baseline method. Meanwhile, the lower root mean square error (RMSE: 6.13) and mean absolute error (MAE: 4.10) indicate that LFCNet has higher counting precision and stability. The generalization studies demonstrate that LFCNet achieves the balance between accuracy and speed in various fish counting scenarios.
Keyword:
Aquaculture
Density map regression
Fish counting
Lightweight model
期刊
IF:
8.9
论文数:
1.0W
被引数:
4.8W
机构
引用论文
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