Evolutionary Tuner and Selective Kernel Attention for Improving YOLOv11 in Underwater Fish Detection and Recognition

Hua-Ching Chen; Hsuan-Ming Feng

doi:10.46604/ijeti.2026.15600

Authors

Hua-Ching Chen School of Information Engineering, Xiamen Ocean Vocational College, Fujian, China
Hsuan-Ming Feng Department of Computer Science and Information Engineering, National Quemoy University, Kinmen, Taiwan, ROC

DOI:

https://doi.org/10.46604/ijeti.2026.15600

Keywords:

evolutionary tuner, hyperparameter optimization, image feature extraction, selective kernel attention

Abstract

This study aims to enhance the accuracy of underwater fish detection by proposing a dual-enhanced YOLOv11 model. The approach leverages two key improvements: First, a selective kernel attention (SKA) mechanism is incorporated into the YOLOv11 architecture to enable dynamic selection of multi-scale convolution kernels, improving adaptability to various target sizes. Second, an evolutionary tuner (ET) is employed for hyperparameter optimization to refine model performance further. The proposed model achieves significant gains over the baseline, with improvements of 2.06% in mean average precision (mAP)@0.5 and 6.30% in mAP@0.5:0.95, attaining final scores of 98.629% and 86.933%, respectively. The dual-enhanced model demonstrates superior accuracy and robustness in complex underwater environments, ultimately achieving a precision of 99.069% and a recall of 95.968%.

References

C. M. Crain, B. S. Halpern, M. W. Beck, and C. V. Kappel, “Understanding and Managing Human Threats to the Coastal Marine Environment,” Annals of the New York Academy of Sciences, vol. 1162, no. 1, pp. 39-62, 2009.

S. P. González-Sabbagh and A. Robles-Kelly, “A Survey on Underwater Computer Vision,” ACM Computing Surveys, vol. 55, no. 13s, pp. 1-39, 2023.

R. H. Thurstan, S. Brockington, and C. M. Roberts, “The Effects of 118 Years of Industrial Fishing on UK Bottom Trawl Fisheries,” Nature Communications, vol. 1, article no. 15, 2010.

Y. Wang, J. Zhang, Y. Cao, and Z. Wang, “A Deep CNN Method for Underwater Image Enhancement,” IEEE International Conference on Image Processing, pp. 1382-1386, 2017.

S. Mittal, S. Srivastava, and J. P. Jayanth, “A Survey of Deep Learning Techniques for Underwater Image Classification,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 6968-6982, 2023.

C. Li, C. Guo, W. Ren, R. Cong, J. Hou, S. Kwong, et al., “An Underwater Image Enhancement Benchmark Dataset and Beyond,” IEEE Transactions on Image Processing, vol. 29, pp. 4376-4389, 2019.

C. Yang, J. Xiang, X. Li, and Y. Xie, “FishDet-YOLO: Enhanced Underwater Fish Detection with Richer Gradient Flow and Long-Range Dependency Capture through Mamba-C2f,” Electronics, vol. 13, no. 18, article no. 3780, 2024.

M. Kaur and S. Vijay, “Deep Learning with Invariant Feature Based Species Classification in Underwater Environments,” Multimedia Tools and Applications, vol. 83, no. 7, pp. 19587-19608, 2024.

X. Qin, C. Yu, B. Liu, and Z. Zhang, “YOLO8-FASG: A High-Accuracy Fish Identification Method for Underwater Robotic System,” IEEE Access, vol. 12, pp. 73354-73362, 2024.

Y. H. Liu, “Feature Extraction and Image Recognition with Convolutional Neural Networks,” Journal of Physics: Conference Series, vol. 1087, no. 6, article no. 062032, 2018.

S. Anwar, C. Li, and F. Porikli, “Deep Underwater Image Enhancement,” https://doi.org/10.48550/arXiv.1807.03528, 2018.

M. A. Iqbal, Z. Wang, Z. A. Ali, and S. Riaz, “Automatic Fish Species Classification Using Deep Convolutional Neural Networks,” Wireless Personal Communications, vol. 116, no. 2, pp. 1043-1053, 2021.

X. Chen, P. Zhang, L. Quan, C. Yi, and C. Lu, “Underwater Image Enhancement Based on Deep Learning and Image Formation Model,” https://doi.org/10.48550/arXiv.2101.00991, 2021.

N. Wang, T. Chen, X. Kong, Y. Chen, R. Wang, Y. Gong, et al., “Underwater Attentional Generative Adversarial Networks for Image Enhancement,” IEEE Transactions on Human-Machine Systems, vol. 53, no. 3, pp. 490-500, 2023.

T. Yu and H. Zhu, “Hyper-Parameter Optimization: A Review of Algorithms and Applications,” https://doi.org/10.48550/arXiv.2003.05689, 2020.

S. Villon, D. Mouillot, M. Chaumont, E. S. Darling, G. Subsol, T. Claverie, et al., “A Deep Learning Method for Accurate and Fast Identification of Coral Reef Fishes in Underwater Images,” Ecological informatics, vol. 48, pp. 238-244, 2018.

A. Jalal, A. Salman, A. Mian, M. Shortis, and F. Shafait, “Fish Detection and Species Classification in Underwater Environments Using Deep Learning with Temporal Information,” Ecological Informatics, vol. 57, article no. 101088, 2020.

M. Liu, W. Zhang, C. Wei, Z. Bao, J. Hu, and J. Zhou, “PLGAT: Underwater Plectropomus leopardus Recognition Using Global Attention Mechanism and Transfer Learning,” IEEE Access, vol. 12, pp. 185149-185159, 2024.

F. Wu, Z. Cai, S. Fan, R. Song, L. Wang, and W. Cai, “Fish Target Detection in Underwater Blurred Scenes Based on Improved YOLOv5,” IEEE Access, vol. 11, pp. 122911-122925, 2023.

Z. H. Xu, W. B. Chen, W. F. Yang, and F. Liu, “Fast Algorithm of Evolutional Learning Neural Network,” Second International Conference on Intelligent System Design and Engineering Application, pp. 262-265, 2012.

S. A. Abro, L. G. Hua, J. A. Laghari, M. A. Bhayo, and A. A. Memon, “Machine Learning-Based Electricity Theft Detection Using Support Vector Machines,” International Journal of Electrical & Computer Engineering (2088-8708), vol. 14, no. 2, pp. 1240-1250, 2024.

A. Ghahremani, S. D. Adams, M. Norton, S. Y. Khoo, and A. Z. Kouzani, “Detecting Defects in Solar Panels Using the YOLO v10 and v11 Algorithms,” Electronics, vol. 14, no. 2, article no. 344, 2025.

X. Li, W. Wang, X. Hu, and J. Yang, “Selective Kernel Networks,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 510-519, 2019.

C. Shorten and T. M. Khoshgoftaar, “A Survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, article no. 60, 2019.

D. P. Tran, G. N. Nguyen, and V. D. Hoang, “Hyperparameter Optimization for Improving Recognition Efficiency of an Adaptive Learning System,” IEEE Access, vol. 8, pp. 160569-160580, 2020.

D. Rathi, S. Jain, and S. Indu, “Underwater Fish Species Classification Using Convolutional Neural Network and Deep Learning,” Ninth International Conference on Advances in Pattern Recognition, pp. 1-6, 2017.

S. Wibowo and A. A. Utama, “Optimization of Tiny YOLOv7 Lightweight Model through Network Structure Re-Parameterization for Coral Reef Fish Identification,” International Conference on ICT for Smart Society, pp. 1-7, 2024.