Automating the assessment of biofouling in images and video footage

Images and annotations used for training the computer vision models in the <i>Automating the assessment of biofouling in images using expert agreement as a gold standard</i> (2021) paper. Please cite this paper if you use this dataset.We include biofouling (SLoF), paint damage (Paint quality), and niche area annotations in the metadata. For biofouling, we use the Simplified Level of Fouling (SLoF) scale0: No fouling organisms, but biofilm or slime may be present.<br>1: Fouling organisms (e.g. barnacles, mussels, seaweed or tubeworms are visible but patchy (1-15% of surface covered).<br>2: A large number of fouling organisms are present (16-100% of surface covered).<br>For paint quality, we use the following scale1: Paint not present or in poor condition (16-100% of surface scratched/corroded/fouled).<br>2: Paint visible and in fair condition or slightly obscured (1-15% of surface scratched/corroded/fouled)<br>3: Paint visible and in good condition.We are also releasing models trained on this dataset. Please see this github page for further information on using them.We also include footage from an underwater ROV inspecting two small vessels, to illustrate the utility of the model.本数据集包含用于训练2021年发表的论文《Automating the assessment of biofouling in images using expert agreement as a gold standard》（以专家共识作为金标准的图像生物污损评估自动化）中计算机视觉模型的图像与标注数据。若使用本数据集，请引用该论文。元数据中涵盖三类标注：生物污损标注（采用SLoF，即Simplified Level of Fouling，简化污损等级体系）、漆面损伤标注（采用Paint quality，即漆面质量体系）以及生境区域标注。其中生物污损的分级标准如下：0级：无污损生物附着，但可能存在生物膜或黏液；1级：可见污损生物（如藤壶、贻贝、海藻或管蠕虫），但呈斑块状分布，覆盖表面积1%~15%；2级：存在大量污损生物，覆盖表面积16%~100%。针对漆面质量，我们采用如下分级标准：1级：漆面缺失或状态极差，表面积16%~100%存在刮擦、腐蚀或污损；2级：漆面可见，状态尚可或轻微受损，表面积1%~15%存在刮擦、腐蚀或污损；3级：漆面可见且状态良好。本数据集同步发布了基于该数据集训练得到的计算机视觉模型，相关使用细节请参阅本项目GitHub页面。此外，为直观展示模型的应用效能，我们还收录了水下遥控无人潜水器（ROV，Remotely Operated Vehicle）巡检两艘小型船舶的影像素材。