Improved YOLOv5s for Small Ship Detection With Optical Remote Sensing Images

Ship detection is of great importance in territorial security and marine environmental protection. However, the accurate detection of small ships is a challenging task in complex environments mainly due to small ships having few features on remote sensing images. In this letter, we propose a small ship detection model based on YOLOv5s. The major features of the proposed model include: 1) a detection layer is added with a shallow feature map in the head and skip connections are employed in the neck to improve the detection accuracy of small ships; 2) a novel and effective hybrid spatial pyramid pooling (HSPP) is proposed to fuse the local and global information of feature maps; 3) a coordinate attention (CA) mechanism is employed in the backbone to augment the representations of small ships, and efficient IOU (EIOU) is used as the loss function for bounding box regression to enhance the localization accuracy of the proposed model; and 4) K-means++ algorithm is used to obtain more reasonable anchors for small ship detection. We introduce the multiscale ship dataset OSSD, which contains 10133 images. Experiments on LEVIR-Ship and OSSD validate the effectiveness of our proposed model. The code and OSSD will be available at https://github.com/wenjieo/Improved-YOLOv5s-for-small-ship-detection.