JSAI2020
Jun 9 - Jun 12, 2020Virtual Meetings
JSAI2020
Jun 9 - Jun 12, 2020Virtual Meetings
[1D4-GS-13-03]Automatic Detection of Marine Plastic by Composite Remote Sensing with Deep Learning
〇Jun Sonoda1, Tomoyuki Kimoto2, Yasushi Kanazawa3(1. National Institute of Technology, Sendai College, 2. National Institute of Technology, Oita College, 3. Toyohashi University of Technology)
Keywords:
marine plastic,deep learning,ground penetrating radar,UAV
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.