期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 14, 期 -, 页码 12348-12360出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2021.3130238
关键词
Feature extraction; Residual neural networks; Detectors; Underwater vehicles; Sea surface; Plastics; Convolution; Deep convolutional neural network; deep-sea debris detection; deep-sea debris detection dataset; sea floor
类别
资金
- National Natural Science Foundation of China [42030406]
- Marine Science and Technology Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) [2018SDKJ0102-8]
- Ministry of Science and Technology of China [2019YFD0901001]
- Natural Science Foundation of Shandong Province [ZR2021MD001]
Marine debris has negative impacts on the marine environment and marine life survival, and the detection method for deep-sea debris is crucial for efficient clean-up. This article establishes an efficient deep-sea debris detection method using deep learning, successfully verifying the performance and applicability of the detection network.
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.
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