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Improved underwater target detection algorithm for YOLOv8
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Quan YUAN1, 2, Qingquan YANG1, 2, Yalong YUAN1, 2, Fengjuan LIU1, 2
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition) | 2025, 37(5) : 729 - 740
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Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition) | 2025, 37(5): 729-740
• Artificial Intelligenceand Big Data •
Improved underwater target detection algorithm for YOLOv8
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Quan YUAN1, 2, Qingquan YANG1, 2, Yalong YUAN1, 2, Fengjuan LIU1, 2
Affiliations
  • 1School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
  • 2Research Center for New Communication Technology Applications, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
doi: 10.3979/j.issn.1673-825X.202412060286
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To address the poor performance of underwater object detection caused by light attenuation and scattering, this paper proposes an enhanced underwater object detection framework based on YOLOv8, named ERMS-YOLOv8, aiming to improve detection accuracy. The backbone is replaced with an efficient vision transformer（EfficientViT）to strengthen feature extraction of underwater organisms and reduce information loss. The neck adopts a reparameterized generalized-directional feature pyramid network（RepGFPN）to enhance the fusion of high-level semantic and low-level spatial features, enabling richer feature representation. A mixed local channel attention for object detection（MLCA）is introduced to integrate channel, spatial, local, and global channel information, thereby boosting the model's representational capacity. Additionally, a scalable intersection over union loss（SIoU）is employed to improve boundary prediction accuracy. Experimental re sults demonstrate that the proposed method achieves mAP values of 83.9% on the UPRC2021 dataset and 84.4% on the DUO dataset, outperforming the original YOLOv8 and exhibiting superior performance in underwater object detection.

YOLOv8  /  EfficientViT  /  RepGFPN  /  attention mechanism  /  loss function
Quan YUAN, Qingquan YANG, Yalong YUAN, Fengjuan LIU. Improved underwater target detection algorithm for YOLOv8[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2025 , 37 (5) : 729 -740 . DOI: 10.3979/j.issn.1673-825X.202412060286
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doi: 10.3979/j.issn.1673-825X.202412060286
  • Receive Date：2024-12-06
  • Online Date：2026-04-16
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  • Received：2024-12-06
  • Revised：2025-09-08
Affiliations
    1School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
    2Research Center for New Communication Technology Applications, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
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