July 27, 2024HeliyonOpen Access

Enhanced deep learning models for automatic fish species identification in underwater imagery

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Deep learning models achieve high-accuracy fish classification in underwater imagery, pairing tailored object detection with optimized convolutional feature extraction networks.
The improved fully convolutional network delivers 99.94 % accuracy and 99.58 % precision, confirming superior detection performance across benchmark evaluations.
Benchmark on Fish4knowledge combines unsharp mask filter preprocessing with squeeze and excitation modules, highlighting practical utility for autonomous marine observation.

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Deep learning evaluation demonstrates automated fish species identification in underwater imagery, indicating robust classification performance for marine monitoring.

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Siri et al. (2024) studied this question.

synapsesocial.com/papers/68e5ed47b6db643587581a0d https://doi.org/10.1016/j.heliyon.2024.e35217

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Implication

Deep learning evaluation demonstrates automated fish species identification in underwater imagery, indicating robust classification performance for marine monitoring.

Enhanced deep learning models for automatic fish species identification in underwater imagery

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Enhanced deep learning models for automatic fish species identification in underwater imagery

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Implication

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