Exploring YOLO-Based Deep Learning Approaches for Fish Detection in Intelligent Aquatic Monitoring Systems
Abstract
The Advancements in precision aquaculture demand robust visual monitoring systems capable of accurate, real-time fish detection in complex underwater environments characterized by turbidity, occlusion, and dynamic illumination. While YOLO (You Only Look Once) architectures have demonstrated high efficiency in object detection tasks, their comparative performance for underwater fish detection remains underexplored, particularly across recent variants such as YOLOv5, YOLOv8, and YOLOv11. This study presents a systematic evaluation of three state-of-the-art YOLO models using a curated GlowFish dataset consisting of 533 annotated images across three fluorescent species. Data were acquired under controlled but visually diverse conditions using multi-angle imaging and standardized illumination. A uniform training pipeline, consistent annotation using the COCO format, and identical hyperparameters were applied across models to ensure fair benchmarking. Key evaluation metrics include precision, recall, mAP@0.5, and mAP@0.5:0.95. Experimental results reveal that YOLOv5 achieved the highest precision (0.963) and mAP@0.5 (0.967), while YOLOv8 delivered superior recall (0.930) and more balanced detection across species classes. YOLOv11 demonstrated architectural potential but showed greater sensitivity to class imbalance and reduced confidence stability. Visual analysis and confusion matrices further confirmed model-specific trade-offs in classification reliability and localization precision. This work contributes critical empirical insights into the selection of YOLO architectures for intelligent aquaculture systems, offering practical guidance for real-time aquatic monitoring deployments. Future research will extend this framework to multi-species, multi-environment datasets, integrate spatiotemporal behavioral tracking, and investigate deployment on resource-constrained edge-AI platforms, advancing the field toward interpretable and autonomous aquatic monitoring solutions.
Downloads
References
J. Dunning, T. Jansen, A. J. Fenwick, and P. G. Fernandes, A new in-situ method to estimate fish target strength reveals high variability in broadband measurements, Fisheries Research, vol. 261, p. 106611, 2023. https://doi.org/10.1016/j.fishres.2023.106611
T. M. Evans et al., Fish avoidance of ships during acoustic surveys tested with quiet uncrewed surface vessels, Fisheries Research, vol. 267, p. 106817, 2023. https://doi.org/10.1016/j.fishres.2023.106817
K. Williams, P. Goddard, R. Wilborn, D. Bryan, and C. Rooper, Fish behavior in response to an approaching underwater camera, Fisheries Research, vol. 268, p. 106823, 2023. https://doi.org/10.1016/j.fishres.2023.106823
N. Shang, C. Zhang, W. Zhang, X. Zhang, and Y. Zhang, Assessment of fish health status for waterless transportation based on image features and deep learning models, Aquaculture, vol. 595, Part 2, p. 741697, 2025. https://doi.org/10.1016/j.aquaculture.2024.741697
B. A. Staton, C. Justice, S. White, E. R. Sedell, L. A. Burns, and M. J. Kaylor, Accounting for uncertainty when estimating drivers of imperfect detection: An integrated approach illustrated with snorkel surveys for riverine fishes, Fisheries Research, vol. 249, p. 106209, 2022. https://doi.org/10.1016/j.fishres.2021.106209
M. Saqib et al., Fishing event detection and species classification using computer vision and artificial intelligence for electronic monitoring, Fisheries Research, vol. 280, p. 107141, 2024. https://doi.org/10.1016/j.fishres.2024.107141
S. İşgüzar, M. Türkoğlu, T. Ateşşahin, and Ö. Dürrani, FishAgePredictioNet: A multi-stage fish age prediction framework based on segmentation, deep convolution network, and Gaussian process regression with otolith images, Fisheries Research, vol. 271, p. 106916, 2024. https://doi.org/10.1016/j.fishres.2023.106916
A. A. Muksit, F. Hasan, M. F. H. Bhuiyan Emon, M. R. Haque, A. R. Anwary, and S. Shatabda, YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environments, Ecological Informatics, vol. 72, p. 101847, 2022. https://doi.org/10.1016/j.ecoinf.2022.101847
W. Bi, X. Sun, J. Li, and Y. Jin, HD-YOLO: The hydroid of Aurelia detector, Marine Pollution Bulletin, vol. 210, p. 117346, 2025. https://doi.org/10.1016/j.marpolbul.2024.117346
Y. T. Lin, J. Hu, and J. Chen, Machine vision approach for monitoring and quantifying fish school migration, Ecological Indicators, vol. 169, p. 112769, 2024. https://doi.org/10.1016/j.ecolind.2024.112769
B. Clinton, A. Amperawan, and T. Dewi, Object detection approach using YOLOv5 for plant species identification, Jurnal Elektronika dan Telekomunikasi (JET), vol. 24, no. 2, pp. 120–128, 2024. https://doi.org/10.55981/jet.643
H. Zhang, Z. Tian, L. Liu, H. Liang, J. Feng, and L. Zeng, Real-time detection of dead fish for unmanned aquaculture by YOLOv8-based UAV, Aquaculture, vol. 595, Part 1, p. 741551, 2025. https://doi.org/10.1016/j.aquaculture.2024.741551
Z. Zhou, Y. Hu, X. Yang, and J. Yang, YOLO-based marine organism detection using two-terminal attention mechanism and difficult-sample resampling, Applied Soft Computing, vol. 153, p. 111291, 2024. https://doi.org/10.1016/j.asoc.2024.111291
C. Zhou, C. Wang, D. Sun, J. Hu, and H. Ye, An automated lightweight approach for detecting dead fish in a recirculating aquaculture system, Aquaculture, vol. 594, p. 741433, 2025. https://doi.org/10.1016/j.aquaculture.2024.741433
A. Paul, R. Machavaram, Ambuj, D. Kumar, and H. Nagar, Smart solutions for capsicum harvesting: Unleashing the power of YOLO for detection, segmentation, growth stage classification, counting, and real-time mobile identification, Computers and Electronics in Agriculture, vol. 219, p. 108832, 2024. https://doi.org/10.1016/j.compag.2024.108832
T. Dewi et al., Fuzzy logic-based control for robot-guided strawberry harvesting: Visual servoing and image segmentation approach, SINERGI, vol. 28, no. 3, pp. 661–668, 2024. https://doi.org/10.22441/sinergi.2024.3.021
Y. Shibata et al., Length estimation of fish detected as non-occluded using a smartphone application and deep learning method, Fisheries Research, vol. 273, p. 106970, 2024. https://doi.org/10.1016/j.fishres.2024.106970
D. Acharya et al., Towards automatic anomaly detection in fisheries using electronic monitoring and automatic identification system, Fisheries Research, vol. 272, p. 106939, 2024. https://doi.org/10.1016/j.fishres.2024.106939
B. C. Weinbach, R. Akerkar, M. Nilsen, and R. Arghandeh, Hierarchical deep learning framework for automated marine vegetation and fauna analysis using ROV video data, Ecological Informatics, vol. 85, p. 102966, 2025. https://doi.org/10.1016/j.ecoinf.2024.102966
A. Yassir, S. J. Andaloussi, Q. Ouchetto, K. Mamza, and M. Serghini, Acoustic fish species identification using deep learning and machine learning algorithms: A systematic review, Fisheries Research, vol. 266, p. 106790, 2023. https://doi.org/10.1016/j.fishres.2023.106790
S. G. Alarcón-Silvas et al., Water quality, water usage, nutrient use efficiency, and growth of shrimp Litopenaeus vannamei in an integrated aquaponic system with basil Ocimum basilicum, Aquaculture, vol. 543, p. 737023, 2021. https://doi.org/10.1016/j.aquaculture.2021.737023
G. P. Suárez-Cáceres, L. Pérez-Urrestarazu, M. Avilés, C. Borrero, J. R. L. Eguíbar, and V. M. J. A. Fernández-Cabanás, Susceptibility to water-borne plant diseases of hydroponic vs. aquaponic systems, Aquaculture, vol. 544, p. 737093, 2021. https://doi.org/10.1016/j.aquaculture.2021.737093
T. Dewi, E. N. Mardiyati, P. Risma, and Y. Oktarina, Hybrid machine learning models for PV output prediction: Harnessing Random Forest and LSTM-RNN for sustainable energy management in aquaponic system, Energy Conversion and Management, vol. 330, p. 119663, 2025. https://doi.org/10.1016/j.enconman.2024.119663
T. Dewi et al., Smart integrated aquaponics system: Hybrid solar-hydro energy with deep learning forecasting for optimized energy management in aquaculture and hydroponics, Energy for Sustainable Development, vol. 85, p. 101683, 2025. https://doi.org/10.1016/j.esd.2024.101683
Y. Oktarina, Z. Nawawi, B. Y. Suprapto, and T. Dewi, Towards ecological sustainability: Harvest prediction in agrivoltaic chili farming with CNN transfer learning, Iraqi Journal of Agricultural Sciences, vol. 55, no. 6, pp. 1910–1926, 2024. https://doi.org/10.36103/5gdhkh84
Y.-T. Lai, Y.-T. Peng, W.-C. Lien, Y.-C. Cheng, Y.-T. Lin, C.-J. Liao, and Y.-S. Chiu, Fully automated learning and predict price of aquatic products in Taiwan wholesale markets using multiple machine learning and deep learning methods, Aquaculture, vol. 586, p. 740741, 2024. https://doi.org/10.1016/j.aquaculture.2024.740741
C. A. Graham et al., A practical evaluation of machine learning for classification of ultrasound images of ovarian development in channel catfish (Ictalurus punctatus), Aquaculture, vol. 552, p. 738039, 2022. https://doi.org/10.1016/j.aquaculture.2022.738039
L. Ortenzi et al., Automated species classification and counting by deep-sea mobile crawler platforms using YOLO, Ecological Informatics, vol. 82, p. 102788, 2024. https://doi.org/10.1016/j.ecoinf.2024.102788
H. Yang, K. Lang, and X. Wang, Identify and segment microalgae in complex backgrounds with improved YOLO, Algal Research, vol. 82, p. 103651, 2024. https://doi.org/10.1016/j.algal.2024.103651
Copyright (c) 2026 EMITTER International Journal of Engineering Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright to this article is transferred to Politeknik Elektronika Negeri Surabaya(PENS) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to PENS. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment. The copyright transfer form can be downloaded here .
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
- Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
- Authors may reproduce or authorize others to reproduce the work or derivative works for the author’s personal use or company use, provided that the source and the copyright notice of Politeknik Elektronika Negeri Surabaya (PENS) publisher are indicated.
- Authors are allowed to use and reuse their articles under the same CC-BY-NC-SA license as third parties.
- Third-parties are allowed to share and adapt the publication work for all non-commercial purposes and if they remix, transform, or build upon the material, they must distribute under the same license as the original.
Plagiarism Check
To avoid plagiarism activities, the manuscript will be checked twice by the Editorial Board of the EMITTER International Journal of Engineering Technology (EMITTER Journal) using iThenticate Plagiarism Checker and the CrossCheck plagiarism screening service. The similarity score of a manuscript has should be less than 25%. The manuscript that plagiarizes another author’s work or author's own will be rejected by EMITTER Journal.
Authors are expected to comply with EMITTER Journal's plagiarism rules by downloading and signing the plagiarism declaration form here and resubmitting the form, along with the copyright transfer form via online submission.
