Large-scale underwater fish recognition via deep adversarial learning
Large-scale underwater fish recognition via deep adversarial learning
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DOI：
10.1007/s10115-021-01643-8
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发表时间：
2022-01
影响因子：
2.7
通讯作者：
Zhixue Zhang;Xiujuan Du;Long Jin;Shuqiao Wang;Lijuan Wang;Xiuxiu Liu
中科院分区：
文献类型：
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作者：
Zhixue Zhang;Xiujuan Du;Long Jin;Shuqiao Wang;Lijuan Wang;Xiuxiu Liu
从水下环境中捕获的图像中识别鱼类在鱼类资源评估、海洋生态系统分析和环境研究等自然科学研究中起着至关重要的作用。然而，水下图像的噪声特性使得训练高性能的鱼类识别模型变得困难。这项工作提出了一种新的深度对抗学习框架AdvFish，用于从嘈杂的大规模水下图像中训练精确的深度神经网络鱼类识别模型。与现有依赖特征工程或隐式机器学习技术来减轻噪声的方法不同，AdvFish是一个最小-最大双层对抗优化框架，通过提出的自适应摄动方法在对抗摄动图像上训练模型。我们表明，在多个基准数据集上，AdvFish比现有方法/模型具有明显的优势，特别是在嘈杂的大规模数据集上。AdvFish是一个通用的学习框架，可以帮助训练更好的识别模型，从极端嘈杂的图像。
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.