Page 251 - 《水产学报》2026年第3期

3 期 水 产 学 报 50 卷

Underwater abalone detection in sea ranch based on improved YOLOv11

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LI Kunda , LIU Zhenlong , WANG Ji 2,3*
(1. School of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang 524088, China;
2. Engineering Technology Research Center for Intelligent Ocean Sensing Network and its Equipment,
Guangdong Ocean University, Zhanjiang 524088, China;
3. School of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China)

Abstract: Aiming at the problems of complex abalone habitat, low visibility with a large amount of noise in the image in the
marine pasture, this study proposes an in-water abalone recognition method YOLOv11-AMSTAR based on the improved You
Only Look Once version 11(YOLOv11) model. The core optimization of the model consists of three aspects: Firstly, a new
enhanced feature extraction module (C3Star) is constructed using Cross Stage Partial with kernel size 2(C3K2) and StarNet,
which enhances the high-dimensional feature representation by star operation, and mines the hidden higher-order correlation
information while preserving the original feature information, thus improving the nonlinear representation and feature differen-
tiation ability of the model. Secondly, the downsampling module Adaptive Downsampling (ADown) is introduced, which
rearranges the dimensionality of the input feature maps and adjusts the fine-grainedness to enhance the ability of the deep net-
work in the model to capture spatial features. Finally, Self-Attention and Convolution mix (ACmix) is added to the neck net-
work to fuse different levels of semantic information, enhance the model's ability to extract and integrate features, and reduce
the interference of cluttered background information. The experimental results show that compared with the original model,
YOLOv11-AMSTAR achieved increases of 5.21% in mAP@0.5, 2.06% in recall rate, 2.66% in accuracy, and 1.79% in
mAP@0.5:0.95 compared to the original model. The study shows that YOLOv11-AMSTAR can significantly enhance the fea-
ture extraction ability of abalone in harsh underwater environments such as low contrast and blur, and significantly improve the
detection precision. This study not only provides an efficient and reliable technical solution for automated and accurate fishing
of underwater organisms, but also its composite improvement strategy for low-quality images and camouflaged targets provides
an important academic reference and application value for solving other target detection problems in similar complex scenes.
Key words: abalone; YOLOv11; underwater target detection; attention module; feature extraction module; downsampling mod-
ule; marine ranch
Corresponding author: WANG Ji. E-mail: 13902576499@163.com

Funding projects: Special Project on New Generation Information Technology in Key Areas of Ordinary Higher Education
Institutions in Guangdong Province (2020ZDZX3008)

https://www.china-fishery.cn 中国水产学会主办 sponsored by China Society of Fisheries
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