Research Assistant Mitsuko Hidaka, Researcher Daisuke Matsuoka, and others at the Information Engineering Program, Research Institute for Value-Added-Information Generation, Japan Agency for Marine-Earth Science and Technology, in collaboration with Associate Professor Shin'ichiro Kako in Research and Education Assembly, Kagoshima University, developed a new method for estimating the coverage area of drifting debris using deep learning (*1). These achievements provided major clues toward the realization of a new coastal environment monitoring system that utilizes artificial intelligence (AI) technologies.
Drifting debris has a significant impact on the marine environment, and poses a major problem to the maintenance of fishing, tourism, and landscapes. To date, although fact-finding surveys of drifting debris along the coast have been conducted worldwide, a versatile and practical technology for quantifying the existing amount of debris has yet to be established.
Therefore, this study applied a deep learning-based image analysis technique called semantic segmentation (*2) to develop a method that detects drifting debris along the coast at a pixel level in photographs taken at ground level with digital cameras. The results indicated that this method can be applied to estimate debris coverage area, and that it has additional applications for coastal images other than the coastal area used for learning or aerial images that were taken using drones. The technology developed herein is expected to see practical application as a universal technology that estimates the existing amount of drifting debris along the coast using photographs taken with a digital camera as well as various coastal monitoring data such as aerial images.
These achievements were published online on February 1 in Marine Pollution Bulletin.
Title:Pixel-level image classification for detecting beach litter using a deep learning approach
Authors:Mitsuko Hidaka1*, Daisuke Matsuoka1*,#, Daisuke Sugiyama1, Koshiro Murakami1, Shin’ichiro Kako2
*Eaually contributed
#Corresponding author
Affiliation:
1. Research Institute for Value-Added-Information Generation(VAiG), Japan Agency for Marine-Earth Science and Technology(JAMSTEC)
2. Graduate School of Science and Engineering, Department of Ocean Civil Engineering, Kagoshima University
URL: https://doi.org/10.1016/j.marpolbul.2022.113371
Furthermore, the training dataset for the AI created in this study was published online in the marine science database catalog SEANOE, which can be downloaded and used free of charge for non-commercial and research purposes only.
Title:The BeachLitter Dataset v2022
Authors:Daisuke Sugiyama1, Mitsuko Hidaka1, Daisuke Matsuoka1#, Koshiro Murakami1, Shin’ichiro Kako2
#Corresponding author
Affiliation:
1. Research Institute for Value-Added-Information Generation(VAiG), Japan Agency for Marine-Earth Science and Technology(JAMSTEC)
2. Graduate School of Science and Engineering, Department of Ocean Civil Engineering, Kagoshima University
URL: https://doi.org/10.17882/85472
【Supplemental information】