This paper studies a reconstruction-based approach for weakly-supervised animal detection from aerial images in marine environments. Such an approach leverages an anomaly detection framework that computes metrics directly on the input space, enhancing interpretability and anomaly localization compared to feature embedding methods. Building upon the success of Vector-Quantized Variational Autoencoders in anomaly detection on computer vision datasets, we adapt them to the marine animal detection domain and address the challenge of handling noisy data. To evaluate our approach, we compare it with existing methods in the context of marine animal detection from aerial image data. Experiments conducted on two dedicated datasets demonstrate the superior performance of the proposed method over recent studies in the literature. Our framework offers improved interpretability and localization of anomalies, providing valuable insights for monitoring marine ecosystems and mitigating the impact of human activities on marine animals.

翻译：本文研究了一种基于重构的弱监督海洋动物检测方法，应用于海洋环境中的航拍图像。该方法利用异常检测框架直接在输入空间上计算度量指标，相较于特征嵌入方法增强了可解释性和异常定位能力。基于向量量化变分自编码器在计算机视觉数据集异常检测中的成功经验，我们将其适配至海洋动物检测领域，并解决了含噪数据的处理难题。为评估本方法，我们将其与现有方法在航拍图像海洋动物检测场景中进行对比。在两个专用数据集上的实验表明，本方法性能优于近年文献中的相关研究。该框架提升了异常的可解释性和定位精度，为监测海洋生态系统及减轻人类活动对海洋动物的影响提供了重要参考。