EMR-YOLO: A Multi-Scale Benthic Organism Detection Algorithm for Degraded Underwater Visual Features and Computationally Constrained Environments

Zou, Dehua; Zhao, Songhao; Zhou, Jingchun; Liu, Guangqiang; Jiang, Zhiying; Xu, Minyi; Fu, Xianping; Liu, Siyuan

doi:10.3390/jmse13091617

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Open AccessArticle

EMR-YOLO: A Multi-Scale Benthic Organism Detection Algorithm for Degraded Underwater Visual Features and Computationally Constrained Environments

by

Dehua Zou

^1,†,

Songhao Zhao

^1,†

,

Jingchun Zhou

²,

Guangqiang Liu

³,

Zhiying Jiang

²,

Minyi Xu

¹

,

Xianping Fu

² and

Siyuan Liu

^1,4,*

¹

College of Marine Engineering, Dalian Maritime University, Dalian 116026, China

²

School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

³

China Academy of Transportation Sciences, Beijing 100029, China

⁴

State Key Laboratory of Maritime Technology and Safety, Dalian Maritime University, Dalian 116026, China

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

J. Mar. Sci. Eng. 2025, 13(9), 1617; https://doi.org/10.3390/jmse13091617

Submission received: 8 July 2025 / Revised: 13 August 2025 / Accepted: 22 August 2025 / Published: 24 August 2025

(This article belongs to the Topic Applications and Development of Underwater Robotics and Underwater Vision Technology, 2nd Edition)

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Abstract

Marine benthic organism detection (BOD) is essential for underwater robotics and seabed resource management but suffers from motion blur, perspective distortion, and background clutter in dynamic underwater environments. To address visual feature degradation and computational constraints, we, in this paper, introduce EMR-YOLO, a deep learning based multi-scale BOD method. To handle the diverse sizes and morphologies of benthic organisms, we propose an Efficient Detection Sparse Head (EDSHead), which combines a unified attention mechanism and dynamic sparse operators to enhance spatial modeling. For robust feature extraction under resource limitations, we design a lightweight Multi-Branch Fusion Downsampling (MBFDown) module that utilizes cross-stage feature fusion and multi-branch architecture to capture rich gradient information. Additionally, a Regional Two-Level Routing Attention (RTRA) mechanism is developed to mitigate background noise and sharpen focus on target regions. The experimental results demonstrate that EMR-YOLO achieves improvements of 2.33%, 1.50%, and 4.12% in

A P

,

A P_{50}

, and

A P_{75}

, respectively, outperforming state-of-the-art methods while maintaining efficiency.

Keywords: benthic organism detection; efficient dynamic sparse detection head; multi-branch fusion downsampling; regional two-level routing attention

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MDPI and ACS Style

Zou, D.; Zhao, S.; Zhou, J.; Liu, G.; Jiang, Z.; Xu, M.; Fu, X.; Liu, S. EMR-YOLO: A Multi-Scale Benthic Organism Detection Algorithm for Degraded Underwater Visual Features and Computationally Constrained Environments. J. Mar. Sci. Eng. 2025, 13, 1617. https://doi.org/10.3390/jmse13091617

AMA Style

Zou D, Zhao S, Zhou J, Liu G, Jiang Z, Xu M, Fu X, Liu S. EMR-YOLO: A Multi-Scale Benthic Organism Detection Algorithm for Degraded Underwater Visual Features and Computationally Constrained Environments. Journal of Marine Science and Engineering. 2025; 13(9):1617. https://doi.org/10.3390/jmse13091617

Chicago/Turabian Style

Zou, Dehua, Songhao Zhao, Jingchun Zhou, Guangqiang Liu, Zhiying Jiang, Minyi Xu, Xianping Fu, and Siyuan Liu. 2025. "EMR-YOLO: A Multi-Scale Benthic Organism Detection Algorithm for Degraded Underwater Visual Features and Computationally Constrained Environments" Journal of Marine Science and Engineering 13, no. 9: 1617. https://doi.org/10.3390/jmse13091617

APA Style

Zou, D., Zhao, S., Zhou, J., Liu, G., Jiang, Z., Xu, M., Fu, X., & Liu, S. (2025). EMR-YOLO: A Multi-Scale Benthic Organism Detection Algorithm for Degraded Underwater Visual Features and Computationally Constrained Environments. Journal of Marine Science and Engineering, 13(9), 1617. https://doi.org/10.3390/jmse13091617

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EMR-YOLO: A Multi-Scale Benthic Organism Detection Algorithm for Degraded Underwater Visual Features and Computationally Constrained Environments

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