Real-time jellyfish classification and detection algorithm based on improved YOLOv4-tiny and improved underwater image enhancement algorithm.

Meijing Gao, Shiyu Li, Kunda Wang, Yang Bai, Yan Ding, Bozhi Zhang, Ning Guan, Ping Wang

Abstract read

In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

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3 · Its place in the literature

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1 citing paper in PubMed.

Research on floating object classification algorithm based on convolutional neural network.Scientific reports · 2024
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5 · Who and what money

Authors and funding

8 authors.

Meijing GaoSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, China. gaomeijing@126.com.

Shiyu LiThe Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.

Kunda WangSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, China.

Yang BaiThe Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.

Yan DingThe Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, China.

Bozhi ZhangThe Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, China.

Ning GuanThe Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.

Ping WangThe Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outbreak of jellyfish blooms poses a serious threat to human life and marine ecology. Therefore, jellyfish detection techniques have earned great interest. This paper investigates the jellyfish detection and classification algorithm based on optical images and deep learning theory. Firstly, we create a dataset comprising 11,926 images. A MSRCR underwater image enhancement algorithm with fusion is proposed. Finally, an improved YOLOv4-tiny algorithm is proposed by incorporating a CBMA module and optimizing the training method. The results demonstrate that the detection accuracy of the improved algorithm can reach 95.01%, the detection speed is 223FPS, both of which are better than the compared algorithms such as YOLOV4. In summary, our method can accurately and quickly detect jellyfish. The research in this paper lays the foundation for the development of an underwater jellyfish real-time monitoring system.

Indexed as

CnidariaScyphozoaAlgorithmsAnimalsComputer SystemsHumansImage Enhancement

Identifiers

PMID37563193

DOI10.1038/s41598-023-39851-7

PMCPMC10415266

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Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.