DeepSeaNet: A Bio-Detection Network Enabling Species Identification in the Deep Sea Imagery
文献类型：期刊论文
| 作者 | Liu, Aiyue3,4; Liu, Yuhai3,4; Xu, Kuidong2 ; Zhao, Feng2; Zhou, Yuan1; Li, Xiaofeng3,4
|
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2024 |
| 卷号 | 62页码:13 |
| 关键词 | Data augmentation deep-sea remotely operated vehicle (ROV) data new species indication real-time object detection network seamount fine-grained dataset |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2024.3359350 |
| 通讯作者 | Li, Xiaofeng(lixf@qdio.ac.cn) |
| 英文摘要 | 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. |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001173250800024 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/185161]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 海洋研究所_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.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Lab Marine Organism Taxon & Phylogeny, Inst Oceanol, Qingdao 266071, Peoples R China 3.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 4.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China |
| 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. |
入库方式： OAI收割
来源：海洋研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。