Arbitrary-oriented ship detection has become challenging due to problems of high resolution, poor imaging clarity, and large size differences between targets in remote sensing images. Most of the existing ship detection methods are difficult to use simultaneously to meet the requirements of high accuracy and speed. Therefore, we designed a lightweight and efficient multi-scale feature dilated neck module in the YOLO11 network to achieve the high-precision detection of arbitrary-oriented ships in remote sensing images. Firstly, multi-scale dilated attention is utilized to effectively capture the multi-scale semantic details of ships in remote sensing images. Secondly, the interaction between the spatial information of remote sensing images and the semantic information of low-resolution features of ships is realized by using the cross-stage partial stage. Finally, the GSConv module is introduced to minimize the loss of semantic information on ship features during transmission. The experimental results show that the proposed method has the advantages of light structure and high accuracy, and the ship detection performance is better than the state-of-the-art detection methods. Compared with YOLO11n, it improves 3.1% of mAP@0.5 and 3.3% of mAP@0.5:0.95 on the HRSC2016 dataset and 1.9% of mAP@0.5 and 1.3% of mAP@0.5:0.95 on the MMShip dataset.
Huang et al. (2024) studied this question.