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  4. Vol 9, Issue 1, 2026

Underwater Sick Fish Detection Based on an Improved YOLOv11n Approach

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DOI: 10.23977/jipta.2026.090101 | Downloads: 0 | Views: 14

Author(s)

Chong Zheng 1, Su Xu 1, Chuande Xu 1, Guangyu Du 1

Affiliation(s)

1 School of Electronic Engineering, Jiangsu Ocean University, Lianyungang, Jiangsu, 222000, China

Corresponding Author

Su Xu

ABSTRACT

To mitigate the issues of missed and false detections arising from scale variations, image blurring and target occlusion in underwater diseased fish detection, this study develops an enhanced YOLOv11n-based algorithm designated as YOLOv11-DFL. The backbone network incorporates the lightweight MobileNetV4, which combines depthwise separable convolution, lightweight attention modules, and multi-scale feature enhancement to balance feature extraction capability with significant reductions in parameters and computation. For the detection layer, the original P5 branch is removed and a P2 branch is added to enhance shallow detail capture, thereby reducing missed and false detections of small targets. The neck network integrates SlimNeck, which uses GSConv for channel splitting to cut redundant computation and VovGSCSP to strengthen multi-scale feature aggregation and gradient transfer, achieving lightweight design while boosting feature expression efficiency and small-target detection accuracy. Finally, the WIM loss function is adopted to improve model generalization and accelerate convergence. Experiments on the self-built sick fish dataset show that compared with the original YOLOv11n, the proposed method increases Precision, Recall, and mAP50 by 4.7%, 9.2%, and 7.0% respectively. Tests on underwater target datasets including RUOD, URPC2019, UDD, and DUO also demonstrate improved precision and recall, verifying the superior performance of YOLOv11-DFL in sick fish detection.

KEYWORDS

YOLOv11n; MobileNetV4; SlimNeck; Loss Function

CITE THIS PAPER

Chong Zheng, Su Xu, Chuande Xu, Guangyu Du. Underwater Sick Fish Detection Based on an Improved YOLOv11n Approach. Journal of Image Processing Theory and Applications (2026) Vol. 9, No.1, 1-13. DOI: http://dx.doi.org/10.23977/jipta.2026.090101.

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