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Research Article | Open Access | Download PDF
Volume 13 | Issue 9 | Year 2026 | Article Id. IJECE-V13I9P104 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I9P104

Advancing Underwater Object Detection: A Comparative Study of YOLOv5s, YOLOv8s, and YOLOv11s with Bias Analysis

Milind Shah, Sandipkumar R Panchal

Received Revised Accepted Published
06 Aug 2026 05 Sep 2026 11 Sep 2026 29 Sep 2026

Citation :

Milind Shah, Sandipkumar R Panchal, "Advancing Underwater Object Detection: A Comparative Study of YOLOv5s, YOLOv8s, and YOLOv11s with Bias Analysis," International Journal of Electronics and Communication Engineering, vol. 13, no. 9, pp. 45-73, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I9P104

Abstract

Environmental distortions and dataset-induced bias are challenges for underwater object detection due to the impact on model generalization. This research paper focuses on bias in the RUOD dataset and compares the performance of YOLOv5s, YOLOv8s, and YOLOv11s with 9,800 training images. Precision, Recall, and mean Average Precision (mAP) were used to perform a comprehensive analysis. The comparative results demonstrate that YOLOv11s has the highest performance when evaluated by mAP@0.5 (0.8721), followed by YOLOv8s and YOLOv5s, with slight differences among the models. The bias analysis of the training and validation sets showed a significant class imbalance, with the dominant classes being fish (17.5%) and echinus (15.15%), and the underrepresented classes being turtle (5.44%) and jellyfish (3.62%). Class-wise evaluation, however, suggests that the accuracy of detecting classes does not directly relate to the frequency of the classes, as there are several classes with low representation that are detected with high accuracy. The results show that there is a complex interplay between the bias in the datasets and the performance of the models, and that architectural improvements are not enough to entirely compensate for the bias effects. This research work gives insights into bias-aware evaluation to enhance underwater object detection systems.

Keywords

Underwater object detection, YOLO, Deep learning, Object detection, Dataset imbalance, Performance evaluation, Computer vision.

References

  1. Nicolas Carion et al., “End-to-End Object Detection with Transformers,” Computer Vision – ECCV 2020, Lecture Notes in Computer Science, vol. 12346, pp. 213-229, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  2. Wei Liu et al., “SSD: Single Shot MultiBox Detector,” Computer Vision – ECCV 2016, Lecture Notes in Computer Science, vol. 9905, pp. 21-37, 2016.
    [    CrossRef] [Google Scholar] [Publisher Link]
  3. Joseph Redmon, and Ali Farhadi, “YOLOv3: An Incremental Improvement,” arXiv preprint, pp. 1-6, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  4. Shaoqing Ren et al., “Object Detection Networks on Convolutional Feature Maps,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 7, pp. 1476-1481, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  5. Zhaohui Zheng et al., “Zone Evaluation: Revealing Spatial Bias in Object Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 12, pp. 8636-8651, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  6. Aharon Azulay, and Yair Weiss, “Why Do Deep Convolutional Networks Generalize So Poorly to Small Image Transformations?,” Journal of Machine Learning Research, vol. 20, no. 184, pp. 1-25, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  7. Anadi Chaman, and Ivan Dokmanić, “Truly Shift-Invariant Convolutional Neural Networks,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, pp. 3772-3782, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  8. Osman Semih Kayhan, and Jan C. van Gemert, “On Translation Invariance in CNNs: Convolutional Layers Can Exploit Absolute Spatial Location,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 14262-14273, 2020. [CrossRef] [Google Scholar] [Publisher Link]
  9. Richard Zhang, “Making Convolutional Networks Shift-Invariant Again,” Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 7324-7334, 2019.
    [    Google Scholar] [Publisher Link]
  10. Sourajit Saha, and Tejas Gokhale, “Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase Sampling,” 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Tucson, AZ, USA, pp. 620-629, 2025. [CrossRef] [Google Scholar] [Publisher Link]
  11. Renato M. Silva et al., “Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey,” Expert Systems with Applications, vol. 292, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. Joshua Siegel, and Georgios Pappas, “Morals, Ethics, and the Technology Capabilities and Limitations of Automated and Self-Driving Vehicles,” AI and Society, vol. 38, no. 1, pp. 213-226, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. Manisha Saini, and Seba Susan, “Tackling Class Imbalance in Computer Vision: A Contemporary Review,” Artificial Intelligence Review, vol. 56, pp. 1279-1335, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  14. Kemal Oksuz et al., “Imbalance Problems in Object Detection: A Review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 10, pp. 3388-3415, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  15. Manisha Saini, and Seba Susan, “Bag-of-Visual-Words Codebook Generation Using Deep Features for Effective Classification of Imbalanced Multi-Class Image Datasets,” Multimedia Tools and Applications, vol. 80, no. 14, pp. 20821-20847, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  16. Joy Buolamwini, and Timnit Gebru, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” Proceedings of the 1st Conference on Fairness, Accountability and Transparency, pp. 77-91, 2018.
    [    Google Scholar] [Publisher Link]
  17. Daeun Lee, and Jinkyu Kim, “Resolving Class Imbalance for LiDAR-Based Object Detector by Dynamic Weight Average and Contextual Ground Truth Sampling,” 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, pp. 682-691, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  18. Mark Mazumder et al., “DataPerf: Benchmarks for Data-Centric AI Development,” Advances in Neural Information Processing Systems, Red Hook, NY, USA, pp. 5320-5347, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  19. Hussain Alibrahim, and Simone A. Ludwig, “Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization,” 2021 IEEE Congress on Evolutionary Computation (CEC), Kraków, Poland, pp. 1551-1559, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  20. Angelina Wang et al., “Revise: A Tool for Measuring and Mitigating Bias in Visual Datasets,” International Journal of Computer Vision, vol. 130, pp. 1790-1810, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  21. Dewant Katare et al., “Analyzing and Mitigating Bias for Vulnerable Road Users by Addressing Class Imbalance in Datasets,” IEEE Open Journal of Intelligent Transportation Systems, vol. 6, pp. 590-604, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  22. Syed Sahil Abbas Zaidi et al., “A Survey of Modern Deep Learning-Based Object Detection Models,” Digital Signal Processing, vol. 126, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  23. Zeran Wang et al., “The Evolution of Object Detection from CNNs to Transformers and Multi-Modal Fusion,” Scientific Reports, vol. 16, pp. 1-20, 2026.
    [    CrossRef] [Google Scholar] [Publisher Link]
  24. Bimsara Pathiraja, Caleb Liu, and Ransalu Senanayake, “Fairness in Autonomous Driving: Towards Understanding Confounding Factors in Object Detection under Challenging Weather,” arXiv preprint, pp. 1-14, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  25. Haiping Ma et al., “Weighted Multi-Error Information Entropy Based You Only Look Once Network for Underwater Object Detection,” Engineering Applications of Artificial Intelligence, vol. 130, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  26. Long Chen et al., “Underwater Optical Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future,” ACM Computing Surveys, vol. 58, no. 3, pp. 1-34, 2026.
    [    CrossRef] [Google Scholar] [Publisher Link]
  27. Joseph L. Walker et al., “Underwater Object Detection Under Domain Shift,” IEEE Journal of Oceanic Engineering, vol. 49, no. 4, pp. 1209-1219, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  28. Haojie Chen et al., “UAMFDet: Acoustic-Optical Fusion for Underwater Multi-Modal Object Detection,” Journal of Field Robotics, vol. 42, no. 2, pp. 970-983, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  29. Shucheng Li, and Xing Peng, “DyAqua-YOLO: A High-Precision Real-Time Underwater Object Detection Model Based on Dynamic Adaptive Architecture,” Frontiers in Marine Science, vol. 12, pp. 1-18, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  30. Jinghua Huang et al., “YOLOv8-UC: An Improved YOLOv8-Based Underwater Object Detection Algorithm,” IEEE Access, vol. 12, pp. 172186-172195, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  31. Wenling Wang, Zhibin Yu, and Mengxing Huang, “Refining Features for Underwater Object Detection at the Frequency Level,” Frontiers in Marine Science, vol. 12, no. 11, pp. 1-11, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  32. Shifeng Zhang et al., “Bridging the Gap between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 9756-9765, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  33. Mingxing Tan, Ruoming Pang, and Quoc V. Le, “EfficientDet: Scalable and Efficient Object Detection,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 10778-10787, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  34. Tsung-Yi Lin et al., “Focal Loss for Dense Object Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 318-327, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  35. Joseph Redmon et al., “You Only Look Once: Unified, Real-Time Object Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 779-788, 2016.
    [    CrossRef] [Google Scholar] [Publisher Link]
  36. Joseph Redmon, and Ali Farhadi, “YOLO9000: Better, Faster, Stronger,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 6517-6525, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  37. Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao, “YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors,” 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, pp. 7464-7475, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  38. Shaoqing Ren et al., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  39. Zhaowei Cai, and Nuno Vasconcelos, “Cascade R-CNN: Delving into High Quality Object Detection,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 6154-6162, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  40. Chenping Fu et al., “Rethinking General Underwater Object Detection: Datasets, Challenges, and Solutions,” Neurocomputing, vol. 517, pp. 243-256, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  41. Yan′e Duan et al., “Review on Visual Attributes Measurement Research of Aquatic Animals Based on Computer Vision,” Transactions of the Chinese Society of Agricultural Engineering, vol. 31, no. 15, pp. 1-11, 2015.
    [    Google Scholar] [Publisher Link]
  42. Muwei Jian et al., “Underwater Object Detection and Datasets: A Survey,” Intelligent Marine Technology and Systems, vol. 2, pp. 1-12, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  43. David B. Gillis, “An Underwater Target Detection Framework for Hyperspectral Imagery,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 1798-1810, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  44. Muwei Jian et al., “Underwater Image Processing and Analysis: A Review,” Signal Processing: Image Communication, vol. 91, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  45. Xiaoting Shi et al., “Underwater Cage Boundary Detection Based on GLCM Features by Using SVM Classifier,” 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Hong Kong, China, pp. 1169-1174, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  46. Huilin Ge et al., “A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures,” Machines, vol. 10, no. 9, pp. 1-16, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  47. Huilin Ge et al., “Single-Stage Underwater Target Detection Based on Feature Anchor Frame Double Optimization Network,” Sensors, vol. 22, no. 20, pp. 1-14, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  48. Fei Lei, Feifei Tang, and Shuhan Li, “Underwater Target Detection Algorithm Based on Improved YOLOv5,” Journal of Marine Science and Engineering, vol. 10, no. 3, pp. 1-19, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  49. Martin Zurowietz, and Tim W. Nattkemper, “Unsupervised Knowledge Transfer for Object Detection in Marine Environmental Monitoring and Exploration,” IEEE Access, vol. 8, pp. 143558-143568, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  50. Dewant Katare et al., “Bias Detection and Generalization in AI Algorithms on Edge for Autonomous Driving,” 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC), Seattle, WA, USA, pp. 342-348, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  51. Chunhui Zhang, and Shenming Gu, “Fish Object Detection Based on Spatial Bias Pyramid Pooling: Improved BIAS-YOLO,” 2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi'an, China, pp. 1976-1979, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  52. Aryan Tummala, Vyaas Baskar, and Sohail H. Zaidi, “Impact of Lighting-Based Biases on the Performance of YOLOv8 Object Detection Models,” 2024 IEEE International Conference on Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA), Bandung, Indonesia, pp. 1-5, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  53. Ross Girshick et al., “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 580-587, 2014.
    [    CrossRef] [Google Scholar] [Publisher Link]
  54. Sandeep Nandal, and Alka Chaudhary, “Objects Relativity Bias: For Detected Objects if They Are in the Range of Their Bigger Compositions,” 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, pp. 1-4, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  55. Singharat Rattanaphan, and Alexia Briassouli, “Evaluating Generalization, Bias, and Fairness in Deep Learning for Metal Surface Defect Detection: A Comparative Study,” Processes, vol. 12, no. 3, pp. 1-32, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  56. Chang Xu et al., “Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 48, no. 3, pp. 3167-3184, 2026.
    [    CrossRef] [Google Scholar] [Publisher Link]
  57. Edwine Nabahirwa et al., “A Structured Review of Underwater Object Detection Challenges and Solutions: From Traditional to Large Vision Language Models,” arXiv preprint, pp. 1-72, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  58. Yifan Wei et al., “RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8,” Applied Sciences, vol. 15, no. 7, pp. 1-21, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]