An Efficient Deep-Sea Debris Detection Method Using Deep Neural Networks

Abstract

Marine debris impacts negatively upon the marine environment and the survival of marine life because they are some difficult-to-degrade substances, and most of them will sink into the deep sea and continue to exist in the ocean. Autonomous underwater vehicles can clean up the deep-sea debris to some extent. However, the efficient detection method plays a critical role in the collection rate. This article establishes an efficient deep-sea debris detection method with high speed using deep learning methods. First, a real deep-sea debris detection dataset (3-D dataset) is established for further research. The dataset contains seven types of debris: cloth, fishing net and rope, glass, metal, natural debris, rubber, and plastic. Second, the one-stage deep-sea debris detection network ResNet50-YOLOV3 is proposed. In addition, eight advanced detection models are also involved in the detection process of deep-sea debris. Finally, the performance of ResNet50-YOLOV3 is verified by experiments. Furthermore, the applicability and effectiveness of ResNet50-YOLOV3 in deep-sea debris detection are proved by the experimental results.