A Small-Ship Object Detection Method for Satellite Remote Sensing Data

Satellite remote sensing technology can achieve real-time observation of ships at sea, and the remote sensing images obtained have the advantages of high contrast and low noise and have become one of the important means of marine monitoring. For the satellite remote sensing image data, there are two main problems: first, remote sensing data class-imbalance problem, and second the existing target detector in the presence of clouds, islands, farmed nets, and other interferences on the small-target ship, and there is a leakage of detection and wrong detection problem. To address the above problems, first, a new dataset containing 3881 images of remotely sensed ships in a variety of complex environments is constructed, which contains a total of 8418 ship instances. Second, we propose CSDP-YOLO for the small-target ship detection method with remote sensing data class imbalance. In order to enhance the performance of neural networks for small-target ship detection in remote sensing images, the innovative CSDP module is proposed, which uses deep large kernel convolution to enhance the sensory field of shallow features and mixes the channel positions using point convolution to obtain a more excellent feature extraction performance. Finally, the MPDIoU loss function is introduced to solve the class-imbalance problem between remote sensing small target ships and the background. We compare the performance with other state-of-the-art algorithms. The experimental results show that the proposed CSDP-YOLO algorithm can significantly improve the performance of small-target ship detection for private datasets. Its average precision, recall, and $\text{AP}_{50}$ are improved to 90.1%, 86.6%, and 91.4%, respectively. For the SSDD public remote sensing dataset, its metrics can reach the highest 93.6%, 93.7%, and 96.8%, respectively.