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Image segmentation and coverage estimation of deep-sea polymetallic nodules based on lightweight deep learning model.

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第一作者： Yue,Hao

第一单位： Key Laboratory of Marine Geology and Metallogeny, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266061, China.

作者： Yue,Hao ^[1] ; Shijuan,Yan ^[2] ; Gang,Yang ^[3] ; Yiping,Luo ^[1] ; Dalong,Liu ^[1] ; Chunhua,Han ^[4] ; Xiangwen,Ren ^[5] ; Dewen,Du ^[5]

作者单位： Key Laboratory of Marine Geology and Metallogeny, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266061, China. ^[1] Key Laboratory of Marine Geology and Metallogeny, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266061, China. yanshijuan@fio.org.cn.;Laboratory for Marine Mineral Resources, Qingdao Marine Science and Technology Center, Qingdao, 266237, China. yanshijuan@fio.org.cn. ^[2] Key Laboratory of Marine Geology and Metallogeny, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266061, China. yanggang@fio.org.cn.;Key Laboratory of Deep Sea Mineral Resource Development, Shandong(Preparatory), Qingdao, China. yanggang@fio.org.cn. ^[3] National Marine Data and Information Service, Tianjin, 300012, China. ^[4] Key Laboratory of Marine Geology and Metallogeny, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266061, China.;Laboratory for Marine Mineral Resources, Qingdao Marine Science and Technology Center, Qingdao, 266237, China. ^[5]

关键词 Deep learning Depth-wise separable convolution MobileNetV3 Polymetallic nodule coverage Semantic segmentation YOLOv7-PMN

DOI 10.1038/s41598-025-89952-8

PMID 40128230

发布时间 2025-03-27

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