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

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.