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A lightweight dual dynamic ship detection network with complex background of inland river

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Abstract

Ship detection plays an increasing role in security surveillance of inland water transport. However, it is often disturbed by environment noises such as water ripples, stronger scattering, and fluctuating weather. Due to the influence of these sophisticated factors, the current target detection algorithms cannot balance speed, accuracy, and model size in the changeable and complex inland river environment well. To solve this problem, this paper proposes a lightweight dual dynamic ship detection network based on YOLOv5s, which has few parameters and achieves high accuracy. Specifically, mixup data augmentation is introduced in training to balance the uneven distribution of different ship types while enhancing ship characteristics. Then a light pyramid split attention (LPSA) module is also designed to extract features with different perceptual fields, which enriches the ship feature information and suppresses the interference factors in the images. Finally, a dynamic cross stage partial (D-CSP) module is designed with dynamic convolution to extract ship features more efficiently by weighting the input computation with multiple convolution kernels before performing the convolution computation. Experimental results demonstrate that our proposed algorithm enhances the F1 value from 79.2% to 85.7%, and increases mAP@0.5 value from 82.4% to 89.0% when compared to original YOLOv5s. These results also clearly indicate the effectiveness of our model in achieving superior detection capabilities through the integration of the D-CSP and LPSA modules while keeping a satisfactory speed and model size.

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Funding

This research was funded in part by the State Key Laboratory of ASIC & System (2021KF010) and National Natural Science Foundation of China (Grant No. 61404083).

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Contributions

Weina Zhou: Conceptualization, Methodology, Resources, Supervision, Writing -review and editing, Project administration. Chengsong Gu:Methodology, Software, Validation, Formal analysis, Investigation,Data curation, Writing original draft, Visualization.

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Correspondence to Weina Zhou.

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Zhou, W., Gu, C. A lightweight dual dynamic ship detection network with complex background of inland river. SIViP 19, 94 (2025). https://doi.org/10.1007/s11760-024-03607-1

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