SeagrassFinder: An Underwater Eelgrass Image Classification Dataset

This dataset is published as part of the publishing of the paper “SeagrassFinder: Deep Learning for Eelgrass Detection and Coverage Estimation in the Wild” in the Journal Ecological Informatics. The dataset is created as a machine learning dataset for training computer vision models to classify the presence of eelgrass. This dataset was created by the main author Jannik Elsäßer as part of his bachelor's thesis. The original video transect data in this dataset comes from DHI A/S work providing By og Havn a “Summer Status” report on the maritime environmental impacts of the Lynetteholm project. More information on the project and the report is available here: https://byoghavn.dk/mediebibliotek/lynetteholm-sommerstatus-2023/The dataset consists of underwater images taken on a sled, dragged through the water by a survey vessel. The camera used is a Subsea HD-Camera made by LH-Camera. Images were created by taking 5 video frames each second, and then randomly sampling. Each image is labeled True or False for eelgrass presence. In total, the dataset consists of 8500 images from 6 different transects, with 4482 images containing eelgrass, and 4042 images not containing eelgrass. All images have been annotated by a both domain-experts, and non-domain experts. Images were annotated using a uniform sampling process. In the occurrence of any disagreement between annotators, images have been removed from the dataset. For more information on the dataset creation, please refer to the corresponding paper. We recommend using one transect as a test dataset, and not using a random split of all images to create the test dataset. When using a random split of all images, a form of data leakage occurs, since some images can be very similar to other images. An unfortunate limitation, we believe caused by the compression of the videos in the camera system, is some frames contain an echo or form of motion trail. This can lead to ghost like eelgrass features in some frames. This should be taken into consideration when applying the dataset in future locations.本数据集随发表于《生态信息学（Ecological Informatics）》期刊的论文《SeagrassFinder：野外鳗草检测与覆盖度估算的深度学习方法》一同发布，旨在构建用于训练计算机视觉模型以分类识别鳗草存在情况的机器学习数据集。 本数据集由第一作者扬尼克·埃尔萨瑟（Jannik Elsäßer）在其学士学位论文研究期间完成构建。数据集内的原始视频样带数据源自DHI A/S为By og Havn提供《Lynetteholm项目海洋环境影响夏季现状报告》的相关工作，该项目与报告的更多信息可通过以下链接获取：https://byoghavn.dk/mediebibliotek/lynetteholm-sommerstatus-2023/ 数据集包含由勘测船拖拽水下雪橇采集的水下图像，所用拍摄设备为LH-Camera品牌的Subsea HD水下高清相机（Subsea HD-Camera）。图像采集流程为每秒提取5帧视频画面，随后进行随机采样。每张图像均标注有“存在鳗草”或“不存在鳗草”的二分类标签。本数据集总计包含来自6条独立样带的8500张图像，其中4482张包含鳗草，剩余4042张未包含鳗草。所有图像均由领域专家与非领域专家共同标注，标注过程采用统一采样规范，若标注者间存在意见分歧，则对应的图像将从数据集中剔除。如需了解数据集构建的更多细节，请参阅对应发表论文。 我们建议采用单条样带作为测试数据集，而非对全部图像进行随机拆分以构建测试集。若采用全量图像随机划分的方式，将引发数据泄露问题，因为部分图像之间可能存在极高的视觉相似性。 本数据集存在一处需注意的局限性：受相机系统内视频压缩流程的影响，部分帧中会出现回声伪影或运动拖影现象，进而在部分帧中产生类似鳗草的虚假特征。在未来将该数据集应用于其他场景时，需充分考虑到此问题。