Autonomous Underwater Vehicles (AUVs) conduct regular visual surveys of marine environments to characterise and monitor the composition and diversity of the benthos. The use of machine learning classifiers for this task is limited by the low numbers of annotations available and the many fine-grained classes involved. In addition to these challenges, there are domain shifts between image sets acquired during different AUV surveys due to changes in camera systems, imaging altitude, illumination and water column properties leading to a drop in classification performance for images from a different survey where some or all these elements may have changed. This paper proposes a framework to improve the performance of a benthic morphospecies classifier when used to classify images from a different survey compared to the training data. We adapt the SymmNet state-of-the-art Unsupervised Domain Adaptation method with an efficient bilinear pooling layer and image scaling to normalise spatial resolution, and show improved classification accuracy. We test our approach on two datasets with images from AUV surveys with different imaging payloads and locations. The results show that generic domain adaptation can be enhanced to produce a significant increase in accuracy for images from an AUV survey that differs from the training images.

翻译：自主水下航行器（AUV）通过定期对海洋环境进行视觉调查，以表征和监测底栖生物的组成与多样性。受限于标注样本数量稀少及涉及大量细粒度类别，机器学习分类器在此任务中的应用面临挑战。此外，不同AUV调查航次获取的图像集之间存在域偏移——由于摄像系统、成像高度、光照条件及水体光学特性的变化，当图像来自其他航次（可能部分或全部上述要素发生改变）时，分类性能会显著下降。本文提出一种框架，用于提升底栖形态物种分类器在跨航次图像分类中的性能。我们将SymmNet这一顶尖无监督域自适应方法与高效双线性池化层及图像缩放技术相结合，以归一化空间分辨率，并证明该方案能有效提升分类精度。我们采用两组来自不同成像载荷与地理位置的AUV调查图像数据集进行验证，结果表明：通过增强通用域自适应方法，可显著提升与训练图像存在差异的AUV航次图像分类准确率。