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Knowledge and Information Systems
Published in: Knowledge and Information Systems 2/2022

29-01-2022 | Regular Paper

Large-scale underwater fish recognition via deep adversarial learning

Authors: Zhixue Zhang, Xiujuan Du, Long Jin, Shuqiao Wang, Lijuan Wang, Xiuxiu Liu

Published in: Knowledge and Information Systems | Issue 2/2022

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Abstract

Fish species recognition from images captured in underwater environments plays an essential role in many natural science studies, such as fish stock assessment, marine ecosystem analysis, and environmental research. However, the noisy nature of underwater images makes it difficult to train high-performance fish recognition models. This work presents a novel deep adversarial learning framework called AdvFish to train accurate deep neural networks fish recognition models from noisy large-scale underwater images. Unlike existing methods that rely on feature engineering or implicit machine learning techniques to mitigate the noise, AdvFish is a min–max bilevel adversarial optimization framework that trains the model on adversarially perturbed images via a proposed adaptive perturbation method. We show, on multiple benchmark datasets, that AdvFish holds a clear advantage over existing methods/models, especially on a noisy large-scale dataset. AdvFish is a generic learning framework that can help train better recognition models from extremely noisy images.
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Metadata
Title
Large-scale underwater fish recognition via deep adversarial learning
Authors
Zhixue Zhang
Xiujuan Du
Long Jin
Shuqiao Wang
Lijuan Wang
Xiuxiu Liu
Publication date
29-01-2022
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 2/2022
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-021-01643-8

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