[M-IS20] Ocean Plastics, an earth science perspective

[MIS20-05] Quantifying the degree of pollution of beaches using an AI technique

*Mitsuko Hidaka¹, Daisuke Sugiyama¹, Daisuke Matsuoka¹, Fumiaki Araki¹, Masafumi Kamachi¹, Yoichi Ishikawa¹ (1.Japan Agency for Marine-Earth Science and Technology )

Keywords:Artificial litter, Beach, Litter monitoring , Artificial Intelligence , Deep-learning , Object detection

Beached marine litter is harmful to both humans and nature. Monitoring litter on beaches solely through human effort is limiting in terms of both time and expenditure constraints. Establishing technologies that can quantify beached marine litter automatically will contribute to mitigation strategies and scientific understanding of litter. Therefore, we have attempted to develop an AI (Artificial Intelligence) system that can automatically estimate the degree of pollution of beaches using a deep-learning method. The core technology is “semantic segmentation”, which gives pixel-wise classification on images. For the training dataset, pairs of the original image and corresponding labels (ground truth) were prepared. We received monitoring data from the local government (Yamagata prefecture) and prepared the training dataset for the deep-learning model based on these images. For the labels, each region of an image is categorised into eight classes: sky, sea, sand beach, artificial litter, natural litter, installed object, and natural object. Using this dataset, we trained a deep-learning model that can classify an image into the eight classes. The mean IoU (Intersection over Union) between predicted results and ground truth was 76% for all classes. We will present preliminary results from the model, and would also like to discuss the detection of plastic litter.