Pixel-level image classification for detecting beach litter using a deep learning approach. (February 2022)

• Record Type: Journal Article
• Authors: Hidaka, Mitsuko; Matsuoka, Daisuke; Sugiyama, Daisuke; Murakami, Koshiro; Kako, Shin'ichiro
• Is Part Of: Marine pollution bulletin. Volume 175(2022)
• Publication Date: 2022-02
• Subjects: Beach litter -- Marine plastics -- AI -- Deep learning -- Image segmentation -- Beach monitoring; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2022.113371
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: Mitigating and preventing beach litter from entering the ocean is urgently required. Monitoring beach litter solely through human effort is cumbersome, with respect to both time and cost. To address this problem, an artificial intelligence technique that can automatically identify different-sized beach litter is proposed. The technique was established by training a deep learning model that enables pixel-wise classification (semantic segmentation) using beach images taken by an observer on the beach. Eight segmentation classes that include two beach litter classes were defined, and the results were qualitatively and quantitatively verified. Segmentation performance was adequately high based on three metrics: Intersection over Union ( IoU ), precision, and recall, although there is room for further improvement. The potency of the method was demonstrated when it was applied to images taken in different places from training data images, and the coverage of artificial litter calculated and discussed using drone images provided ground truth. Highlights: A technique for the pixel-wise classification of beach litter is proposed. A deep learning model was trained to enable different-sized litter identification. Semantic segmentation was conducted using images taken by an observer on the beach. Segmentation results were qualitatively and quantitatively verified. Application to litter coverage estimation and practical usage were discussed.