用可解释的视觉模型实现濒危鱼种全生命周期无创性别识别
An interpretable framework using foundation models for fish sex identification
- 基于视觉大模型提取鱼体区域，结合原型网络实现可解释识别
- 早期产卵期与产后期准确率分别达74.40%和81.16%
- 适合需要无创、可解释性识别的水产保护研究者使用
准确识别鱼类性别对优化水产养殖育种与管理策略至关重要，尤其对濒危物种。现有方法多为侵入式或应激性，可能引发额外死亡，威胁受胁鱼类种群。为此，本文提出FishProtoNet——一种针对加州特有濒危物种太平洋小银汉鱼（Hypomesus transpacificus）全生命周期的非侵入式计算机视觉框架。不同于传统深度学习方法，该框架通过学习原型表征实现可解释性，并利用视觉基础模型降低背景噪声影响，提升鲁棒性。其包含三个核心组件：基于视觉基础模型的鱼体感兴趣区域（ROIs）提取、鱼体特征提取及基于可解释原型网络的性别识别。在早期产卵期与产后期，准确率分别达到74.40%和81.16%，对应F1分数为74.27%和79.43%。然而，幼鱼阶段因形态差异不显著，识别仍具挑战。代码已公开于https://github.com/zhengmiao1/Fish_sex_identification。
原文摘要 · Abstract (English)
Accurate sex identification in fish is vital for optimizing breeding and management strategies in aquaculture, particularly for species at the risk of extinction. However, most existing methods are invasive or stressful and may cause additional mortality, posing severe risks to threatened or endangered fish populations. To address these challenges, we propose FishProtoNet, a robust, non-invasive computer vision-based framework for sex identification of delta smelt (Hypomesus transpacificus), an endangered fish species native to California, across its full life cycle. Unlike the traditional deep learning methods, FishProtoNet provides interpretability through learned prototype representations while improving robustness by leveraging foundation models to reduce the influence of background noise. Specifically, the FishProtoNet framework consists of three key components: fish regions of interest (ROIs) extraction using visual foundation model, feature extraction from fish ROIs and fish sex identification based on an interpretable prototype network. FishProtoNet demonstrates strong performance in delta smelt sex identification during early spawning and post-spawning stages, achieving the accuracies of 74.40% and 81.16% and corresponding F1 scores of 74.27% and 79.43% respectively. In contrast, delta smelt sex identification at the subadult stage remains challenging for current computer vision methods, likely due to less pronounced morphological differences in immature fish. The source code of FishProtoNet is publicly available at: https://github.com/zhengmiao1/Fish_sex_identification
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