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This research aims to improve automated fish identification for aquaculture using a novel network design framework.

March 2, 2026Ecological InformaticsOpen Access

A design space-based network for efficient and accurate fish recognition in aquaculture

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This research aims to improve automated fish identification for aquaculture using a novel network design framework.
Developed a network exploration framework based on the RegNet design paradigm
Created task-specific architectures for underwater fish recognition
Utilized a small dataset with approximately 200K training iterations
Optimized performance using convolutional networks with attention and group convolution
Achieved a rank-1 accuracy of 95.97% and a TAR of 88.04% at a FAR of 1e-6 on the primary dataset
Reached a rank-1 accuracy of 98.78% and a TAR of 98.71% on a closed-set dataset at the same FAR threshold
Outperformed state-of-the-art models with higher accuracy and reduced GPU usage

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Demonstrates an innovative network framework for fish recognition in aquaculture, highlighting efficiency and accuracy.

Luo et al. (2026) studied this question.

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