An Efficient Deep-Sea Debris Detection Method Using Deep Neural Networks
Bing Xue, Baoxiang Huang, Weibo Wei, Ge Chen, Haitao Li, Nan Zhao, Hongfeng Zhang
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 paper establishes an efficient deep-sea debris detection method with high speed using deep learning methods. First, a real deep-sea debris detection dataset (3D dataset) is established for further research. The dataset contains 7 types of debris: cloth, fishing net &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.