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Volume 19, Issue 4
  • ISSN: 1872-2121
  • E-ISSN: 2212-4047

Abstract

Introduction

In the target detection technology of underwater robots, many patents and papers have aimed to enhance the accuracy of underwater target detection, but limited resources in underwater robots overlook lightweight detection methods.

Methods

In this study, we proposed an underwater target detection method using lightweight devices while ensuring high accuracy that could be maintained with limited resources. Our proposed algorithm leveraged the Ghost lightweight network, EMA mechanism, and CARAFE up-sampling technology to enhance YOLOv5s. To validate our method, comparative experiments, visual analysis, and ablation experiments were conducted.

Results

The experimental results showed that our algorithm had a model size of only 9.7 M, with 4.38×106 parameters and a computational volume of 8.4 GFLOPs. Precision, recall, and [email protected] increased by 4.2%, 2.2%, and 2.5%, respectively.

Conclusion

Our improved algorithm provided an efficient and accurate solution for underwater robot target detection technology.

© 2025 Bentham Science Publishers
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2024-04-26
2025-01-18

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/content/journals/eng/10.2174/0118722121294044240422063140
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Lightweight Underwater Target Detection Method based on Improved YOLOv5s
Recent Pat Eng 19, E260424229345 (2025); https://doi.org/10.2174/0118722121294044240422063140
/content/journals/eng/10.2174/0118722121294044240422063140
/content/journals/eng/10.2174/0118722121294044240422063140
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  • Article Type:     Research Article
Keyword(s): CARAFE; EMA; Ghost; lightweight; Underwater target detection; YOLOv5s

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