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Plankton detection with adversarial learning and a densely connected deep learning model for class imbalanced distribution
Li Y( 李岩)1,2; Guo JH(郭家宏)1,2,3; Guo XM(郭晓敏)1,2,4; Hu ZQ(胡志强)1,2; Tian Y(田宇)1,2
Department海洋机器人前沿技术中心
Source PublicationJournal of Marine Science and Engineering
ISSN2077-1312
2021
Volume9Issue:6Pages:1-14
Indexed BySCI
WOS IDWOS:000666151900001
Contribution Rank1
Funding OrganizationNational Key Research and Development Program of China, grant number No. 2016YFC0300801 ; iLiaoning Provincial Natural Science Foundation of China, grant number 2020-MS-031 ; National Natural Science Foundation of China, grant number 61821005,51809256 ; State Key Laboratory of Robotics at Shenyang Institute of Automation, grant number 2021-Z08 ; LiaoNing Revitalization Talents Program, grant number No. XLYC2007035
KeywordAdversarial learning Class imbalanced distribution Data augmentation Deep learning Plankton detection
Abstract

Detecting and classifying the plankton in situ to analyze the population diversity and abundance is fundamental for the understanding of marine planktonic ecosystem. However, the features of plankton are subtle, and the distribution of different plankton taxa is extremely imbalanced in the real marine environment, both of which limit the detection and classification performance of them while implementing the advanced recognition models, especially for the rare taxa. In this paper, a novel plankton detection strategy is proposed combining with a cycle-consistent adversarial network and a densely connected YOLOV3 model, which not only solves the class imbalanced distribution problem of plankton by augmenting data volume for the rare taxa but also reduces the loss of the features in the plankton detection neural network. The mAP of the proposed plankton detection strategy achieved 97.21% and 97.14%, respectively, under two experimental datasets with a difference in the number of rare taxa, which demonstrated the superior performance of plankton detection comparing with other state-of-the-art models. Especially for the rare taxa, the detection accuracy for each rare taxa is improved by about 4.02% on average under the two experimental datasets. Furthermore, the proposed strategy may have the potential to be deployed into an autonomous underwater vehicle for mobile plankton ecosystem observation.

Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/29196
Collection海洋机器人前沿技术中心
Corresponding AuthorTian Y(田宇)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China
3.School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
4.School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, 110159, China
Recommended Citation
GB/T 7714
Li Y,Guo JH,Guo XM,et al. Plankton detection with adversarial learning and a densely connected deep learning model for class imbalanced distribution[J]. Journal of Marine Science and Engineering,2021,9(6):1-14.
APA Li Y,Guo JH,Guo XM,Hu ZQ,&Tian Y.(2021).Plankton detection with adversarial learning and a densely connected deep learning model for class imbalanced distribution.Journal of Marine Science and Engineering,9(6),1-14.
MLA Li Y,et al."Plankton detection with adversarial learning and a densely connected deep learning model for class imbalanced distribution".Journal of Marine Science and Engineering 9.6(2021):1-14.
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