DeepSeaNet: A Bio-Detection Network Enabling Species Identification in the Deep Sea Imagery

doi:10.1109/TGRS.2024.3359350

IOCAS-IR > 海洋环流与波动重点实验室

	DeepSeaNet: A Bio-Detection Network Enabling Species Identification in the Deep Sea Imagery
	Liu, Aiyue 1,2; Liu, Yuhai 1,2; Xu, Kuidong3 ; Zhao, Feng 3; Zhou, Yuan 4; Li, Xiaofeng 1,2
	2024
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	62 页码:13
通讯作者	Li, Xiaofeng([email protected])
摘要	The detection and preservation of marine biodiversity have garnered global attention. The incorporation of deep learning methodologies can elevate the efficiency of species detection. In this study, we developed a DeepSeaNet for effective localization and accurate classification of organisms based on deep-sea images, as well as for hinting at unknown organisms (new species). The DeepSeaNet fully accommodates the unique characteristics of deep-sea organisms and imaging environment, leading to remarkable advancements in fine-grained analysis and accuracy. The DeepSeaNet comprises two network components: a deep-sea classes detection network (CDN) and an unsupervised species clustering network (SCN). CDN is used for biological class detection and is specifically tailored for deep-sea environments. It incorporates modules for feature fusion, multiscale analysis, and self-attention. SCN is specifically designed to detect and identify new species by utilizing the location information extracted from the CDN output results. It is composed of a feature extraction module and a clustering module. By collecting deep-sea image data from the "KeXue" Science Research Vessel, we constructed a dataset totaling 29 436 images of deep-sea organisms covering more than 500 species of deep-sea seamount organisms. This dataset serves as the foundational dataset for our experiment. As a result, our model achieves an 82.18% mean average precision (mAP) for class detection and a 43.4% accuracy for species detection. Furthermore, the model has the capability to identify new species through the computation of interspecies distances.
关键词	Data augmentation deep-sea remotely operated vehicle (ROV) data new species indication real-time object detection network seamount fine-grained dataset
DOI	10.1109/TGRS.2024.3359350
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001173250800024
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/185162
专题	海洋环流与波动重点实验室海洋生物分类与系统演化实验室
通讯作者	Li, Xiaofeng
作者单位	1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 3.Chinese Acad Sci, Lab Marine Organism Taxon & Phylogeny, Inst Oceanol, Qingdao 266071, Peoples R China 4.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
第一作者单位	海洋环流与波动重点实验室
通讯作者单位	海洋环流与波动重点实验室
推荐引用方式 GB/T 7714	Liu, Aiyue,Liu, Yuhai,Xu, Kuidong,et al. DeepSeaNet: A Bio-Detection Network Enabling Species Identification in the Deep Sea Imagery[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:13.
APA	Liu, Aiyue,Liu, Yuhai,Xu, Kuidong,Zhao, Feng,Zhou, Yuan,&Li, Xiaofeng.(2024).DeepSeaNet: A Bio-Detection Network Enabling Species Identification in the Deep Sea Imagery.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,13.
MLA	Liu, Aiyue,et al."DeepSeaNet: A Bio-Detection Network Enabling Species Identification in the Deep Sea Imagery".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):13.

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