Objectives To enhance the stability and accuracy of intelligent ships in the automatic detection of floating objects on the water surface, this study proposes a water surface multi-target detection method of intelligent ship based on an improved YOLOv8 algorithm.

Methods To improve the model's detection capability across varying object scales, a DDCNv4 module is designed to replace the C2f module in the Backbone network. At the connection with the Neck layer, a bi-level routing attention (BRA) mechanism is introduced to enhance the model's anti-interference performance and texture feature extraction, particularly optimizing the detection of small-scale objects. In addition, the DSConv module is further integrated into the Neck layer to achieve a lightweight model architecture that balances the model performance and computational efficiency.

Results Experimental results show that the proposed improved YOLOv8 algorithm achieves a detection accuracy of 90.4%, outperforming YOLOv5, YOLOv7, and the original YOLOv8. This represents a 5.6% improvement in mAP@0.5 over the original YOLOv8 algorithm, while reducing the number of parameters by 0.1×10⁶ and increasing the inference speed by 1.98 frames per second (FPS).

Conclusions The prosed algorithm maintains high detection performance in the complex water surface environments, providing a theoretical reference for the application of target detection in the field of intelligent ships.