Research on Ship Target Detection Based on YOLOv8
DOI:
https://doi.org/10.53469/wjimt.2025.08(09).14Keywords:
YOLOv8, Ship Target Detection, Deep Learning, Intelligent MaritimeAbstract
With the rapid development of the marine economy, maritime traffic supervision and safety management have raised higher requirements for ship detection. Traditional methods suffer from low efficiency and high false detection rates, making it difficult to meet the needs of intelligent maritime management. This paper proposes a ship target detection model based on YOLOv8. Experimental results show that the model achieves 99.15% mAP@50 and 85.14% mAP@50:95, effectively handling ship recognition tasks under complex sea conditions, providing reliable technical support for smart ocean construction and maritime supervision.
References
Zhou, Zhenzhen et al. "A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning", Journal of marine science and engineering 11.7 (2023).
Li, Haoxiang et al. "A Convolutional Neural Network Cascade for Face Detection.", Computer Vision and Pattern Recognition (2015): 5325-5334.
He, Kaiming et al. "Deep Residual Learning for Image Recognition", Computing Research Repository (2016): 770-778.
Huang G, Liu Z, Laurens V D M,et al. Densely Connected Convolutional Networks [J]. IEEE Computer Society, 2016.DOI:10.1109/CVPR.2017.243.
Varghese, Rejin, and Sambath M.. "YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness", 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) (2024): 1-6.
Woo S, Park J, Lee J Y, et al. CBAM: Convolutional Block Attention Module [J]. Springer, Cham, 2018. DOI:10.1007/978-3-030-01234-2_1.
Li J, Qu C, Peng S, et al. Ship detection in SAR images based on convolutional neural network [J]. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2018, 40(9):1953-1959.
Tang, Xiao et al. "DBW-YOLO: A High-Precision SAR Ship Detection Method for Complex Environments", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17 (2024): 7029-7039.
Shao Z, Wu W, Wang Z, et al. SeaShips: A Large-Scale Precisely-Annotated Dataset for Ship Detection [J]. IEEE Transactions on Multimedia, 2018, 20(10):1-1.
Zheng, Yanmei, Guanghui Zhou, and Bibo Lu. "Rebar Cross-section Detection Based on Improved YOLOv5s Algorithm." Innovation & Technology Advances 1.1 (2023): 1-6. https://doi.org/10.61187/ita.v1i1.1
Zhao, Xiujuan, Lei Zhang, and Zhonghua Hu. "Smart warehouse track identification based on Res2Net-YOLACT+ HSV." Innovation & Technology Advances 1.1 (2023): 7-11. https://doi.org/10.61187/ita.v1i1.2