The BeachLitter dataset for image segmentation of beach litter

This dataset consists of 3500 images of beach litter and 3500 corresponding pixel-wise labelled images. Although performing such pixel-by-pixel semantic masking is expensive, it allows us to build machine-learning models that can perform more sophisticated automated visual processing. We believe this dataset may be of significance to the scientific communities concerned with marine pollution and computer vision, as this dataset can be used for benchmarking in the tasks involving the evaluation of marine pollution with various machine learning models. The beach litter images were obtained from coastal environment surveys conducted between 2011 and 2019 by the Yamagata Prefectural Government, Japan. These images were originally obtained owing to the reporting guidelines concerning regular coastal-environmental-cleanup and beach-litter-monitoring surveys. Based on these images, the Japan Agency for Marine-Earth Science and Technology created 3500 images comprising eight classes of semantic masks for beach litter detection

本数据集包含3500张海滩垃圾图像，以及与之对应的3500张像素级标注图像。尽管逐像素生成语义掩码的标注成本高昂，但该标注方式可助力我们构建可实现更复杂自动化视觉处理的机器学习模型。我们认为本数据集对于海洋污染与计算机视觉领域的科研群体具有重要意义，因其可被用于各类依托机器学习模型开展海洋污染评估任务的基准测试。本数据集内的海滩垃圾图像采集自日本山形县厅于2011年至2019年间开展的沿海环境调查。这些图像最初是依据常规沿海环境清理与海滩垃圾监测调查的报告规范获取的。基于这些图像，日本海洋·地球科学技术振兴机构（Japan Agency for Marine-Earth Science and Technology）制作了3500张涵盖8类语义掩码的图像，用于海滩垃圾检测任务。