Multi-scale spatial fusion lightweight model for optical remote sensing image-based small object detection

Current remote sensing object detection frameworks often focus solely on the geometric relationship between true and predicted boxes, neglecting the intrinsic shapes of the boxes. In the field of remote sensing detection, there are numerous elongated bounding boxes. Variations in the shape and size of these boxes result in differences in their Intersection over Union (IoU) values, which is particularly noticeable when detecting small objects. Platforms with limited resources, such as satellites and unmanned drones, have strict requirements for detector storage space and computational complexity. This makes it challenging for existing methods to balance detection performance and computational demands. Therefore, this paper presents RS-YOLO, a lightweight framework that enhances You Only Look Once (YOLO) and is specifically designed for deployment on resource-limited platforms. RS-YOLO has developed a bounding box regression approach for remote sensing images, focusing on the shape and scale of the boundary boxes. Additionally, to improve the integration of multi-scale spatial features, RS-YOLO introduces a lightweight multi-scale hybrid attention module for cross-space fusion. The DOTA-v1.0 and HRSC2016 datasets were used to test our model, which was then compared to multiple state-of-the-art oriented object detection models. The results indicate that the detector introduced in this article achieves top performance while being lightweight and suitable for deployment on resource-limited platforms.