RSIDetNet: An Efficient Oriented Small Object Detection Model for Remote Sensing Images Based on Cross-Scale Feature Fusion and Large Kernel Decomposition

Small object detection in remote sensing images is crucial for maximizing data utility, but small objects face challenges due to their limited pixel coverage, low resolution, and susceptibility to background noise. This paper proposes an orientated small object detection model for remote sensing images based on cross-scale feature fusion and large kernel decomposition. The model consists of four main components: the image feature extraction module, the multi-scale feature fusion module, the cross-fusion region proposal network for generating candidate regions, and the dual detection head for predicting target categories and rotating bounding boxes. Experiments are conducted on two datasets, SODA‐A and HRSC‐2016, and the results show that the proposed model improves the mean average precision (mAP) by at least 6.3% over classical 1‐stage models and by at least 2.6% over classical 2‐stage model. In particular, when detecting very small objects (area less than 144 pixels), the mAP value is as high as 17.2%, which is a significant improvement compared with other models, indicating that it is very effective in dealing with the difficult task of small object detection.