PAG-YOLO: A Portable Attention-Guided YOLO Network for Small Ship Detection

The YOLO network has been extensively employed in the field of ship detection in optical images. However, the YOLO model rarely considers the global and local relationships in the input image, which limits the final target prediction performance to a certain extent, especially for small ship targets. To address this problem, we propose a novel small ship detection method, which improves the detection accuracy compared with the YOLO-based network architecture and does not increase the amount of computation significantly. Specifically, attention mechanisms in spatial and channel dimensions are proposed to adaptively assign the importance of features in different scales. Moreover, in order to improve the training efficiency and detection accuracy, a new loss function is employed to constrain the detection step, which enables the detector to learn the shape of the ship target more efficiently. The experimental results on a public and high-quality ship dataset indicate that our method realizes state-of-the-art performance in comparison with several widely used advanced approaches.

Automated summary

This study proposes a novel small ship detection method that improves accuracy by employing attention mechanisms in spatial and channel dimensions. The introduction of a new loss function to constrain the detection step enhances training efficiency and achieves state-of-the-art performance compared to advanced methods.