Data from: Improving automated annotation of benthic survey images using wide-band fluorescence

Large-scale imaging techniques are used increasingly for ecological surveys. However, manual analysis can be prohibitively expensive, creating a bottleneck between collected images and desired data-products. This bottleneck is particularly severe for benthic surveys, where millions of images are obtained each year. Recent automated annotation methods may provide a solution, but reflectance images do not always contain sufficient information for adequate classification accuracy. In this work, the FluorIS, a low-cost modified consumer camera, was used to capture wide-band wide-field-of-view fluorescence images during a field deployment in Eilat, Israel. The fluorescence images were registered with standard reflectance images, and an automated annotation method based on convolutional neural networks was developed. Our results demonstrate a 22% reduction of classification error-rate when using both images types compared to only using reflectance images. The improvements were large, in particular, for coral reef genera Platygyra, Acropora and Millepora, where classification recall improved by 38%, 33%, and 41%, respectively. We conclude that convolutional neural networks can be used to combine reflectance and fluorescence imagery in order to significantly improve automated annotation accuracy and reduce the manual annotation bottleneck.

大规模成像技术在生态调查中的应用日趋广泛。然而人工分析的成本往往高得难以承受，在已采集图像与目标数据产品之间形成了显著瓶颈。这一问题在底栖生物调查中尤为突出——此类调查每年可产出数百万张图像。近期的自动标注方法或可提供破解路径，但反射率图像往往无法提供足够信息以达到令人满意的分类精度。本研究中，研究人员采用经过改装的低成本民用相机FluorIS（FluorIS），在以色列埃拉特的野外部署场景中采集了宽带宽视场荧光图像。随后将荧光图像与标准反射率图像进行配准，并开发了一种基于卷积神经网络（Convolutional Neural Networks）的自动标注方法。研究结果显示，相较于仅使用反射率图像的方案，同时使用两类图像可将分类错误率降低22%。该改进效果尤为显著，针对珊瑚礁属的扁脑珊瑚属（Platygyra）、鹿角珊瑚属（Acropora）与千孔珊瑚属（Millepora），其分类召回率分别提升38%、33%与41%。综上，卷积神经网络可用于融合反射率与荧光图像，从而显著提升自动标注精度，缓解人工标注带来的瓶颈制约。