Abstract
Beaches are recognized as major sinks of plastic litter and key sites where litter fragments into countless small pieces. Because those fine particles are almost impossible to remove from the natural environment, it is essential to monitor macroplastic litter on beaches before they degrade. To observe the distribution of this litter in detail, it is essential to have automated and objective image-processing methods that can be applied to images captured by remote sensing devices, such as web cameras and drones. To develop such an automated analysis method, a deep learning-based approach has recently become mainstream, and clarifying technical issues based on case studies is vital. The preparation of training data for those practices is critical but laborious. The BePLi Dataset v2 is updated from BePLi Dataset v1, comprises 3722 original images of beach plastic litter and 118,572 manually processed annotations. All original images were obtained from the natural coastal environment on the Northwest Japan coast, and annotation for plastic litter was provided at both the pixel and individual levels. The plastic litter objects are categorized into thirteen representative plastic object classes: "pet_bottle," "other_bottle," "plastic_bag," "box_shaped_case," "other_container," "rope," "other_string," "fishing_net," "buoy," "other_fishing_gear," "styrene_foam," "others" and "fragment." The BePLi Dataset v2 allows users to develop an instance segmentation and object detection method detecting macro beach plastic litter individually and at the pixel level. Depending on the user, this dataset can serve multiple purposes at different levels of technology development, from counting objects to estimating litter coverage, as it provides both bounding box- and pixel-based annotations.