Small object detection in remote sensing images through multi-scale feature fusion

Abstract Due to the challenges posed by background noise and the limited information available for small targets in remote sensing images, the detection performance for such targets remains unsatisfactory. To address these issues and enhance detection accuracy, we propose an improved algorithm based on RTDETR, named Adaptive Selective Transformer. Firstly, in the feature extraction network, we introduce an adaptive convolutional feature enhancement module to improve the multi-scale feature extraction capability in low-resolution remote sensing images. Secondly, we design a multi-scale enhancement structure to extract detailed information from small target images through enhanced multi-scale representation learning, thereby generating target features with stronger discriminative power. Finally, we propose a hierarchical frequency attention mechanism to achieve localized enhancement of contextual awareness, effectively capturing high-frequency local feature information of small targets. Experimental results demonstrate that the Adaptive Selective Transformer achieves superior small target detection performance, validating the effectiveness of our modifications to the original RTDETR model.