Automatic Detection of Marine Plastic by Composite Remote Sensing with Deep Learning

• SONODA Jun

  National Institute of Technology, Sendai College

• KIMOTO Tomoyuki

  National Institute of Technology, Oita College

• KANAZAWA Yasushi

  Toyohashi University of Technology

Bibliographic Information

Other Title

• 複合リモートセンシングと深層学習を用いた海洋プラスチックの自動検出

Abstract

In recent years, marine plastic has become a world problem. In this study, we have developed an automatic detection method for the marine plastic in/on the beach by the ground-penetrating radar (GPR) and the unmanned aerial vehicle (UAV) images with the deep learning. We have generated the GPR images for training using a fast finite-difference time-domain (FDTD) simulation with graphics processing units (GPUs). Also, we have made the training images of plastics by UAV images. The training images have been learned by a 5-layers convolutional neural network (CNN) and the YOLOv3. We have shown that unlearned plastics images in/on the beach can be detected with 95% accuracy by using our proposed method.

Journal

• Proceedings of the Annual Conference of JSAI

  Proceedings of the Annual Conference of JSAI JSAI2020 (0), 1D4GS1303-1D4GS1303, 2020

  The Japanese Society for Artificial Intelligence

Keywords

• marine plastic
• deep learning
• ground penetrating radar
• UAV

Details

• CRID: 1390003825189212032
• NII Article ID: 130007856659
• DOI: 10.11517/pjsai.jsai2020.0_1d4gs1303
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed