Robust and Real-Time Ship Object Detection Method Based on Enhanced CNN

Ship detection holds a pivotal position in intelligent maritime transportation systems, ensuring the stable development of maritime engineering. However, the complex marine environment and practical deployment requirements pose numerous challenges to ship detection tasks. To address these limitations, this paper proposes a robust and real-time ship object detection method based on enhanced CNN (RRSD). Firstly, an efficient feature extraction module (EFEM) is proposed, which introduces a cross-stage stacked enhanced multi-head self-attention to reduce the computational complexity of the model while enhancing the network's feature extraction capability. Next, a feature fusion network (FFM) is introduced to enhance the feature representation capability of small objects by effectively aggregating features of different scales in the backbone. To focus on the attention regions in the image, designing a lightweight enhanced attention module (EAM) to improve the model's inference speed and classification ability. Additionally, by utilizing data augmentation techniques without any additional cost, the overall performance of the model is intuitively and efficiently improved. The proposed method is extensively experimented on a self-built ship detection dataset (SDD2024) through qualitative and quantitative experiments. The experimental results indicate that RRSD achieves a detection performance of 88.01mAP50 (mean average precision at an intersection over unionof 50) and 7.8ms, significantly outperforming object detection models of the same scale, and achieving a good trade-off between detection accuracy and speed. Furthermore, robustness experiments and embedded deployment tests demonstrate its good detection accuracy, fast speed, and satisfactory robustness in ship detection tasks, which basically meet the practical requirements of deployment and implementation.