SADFF-Net: Scale-Aware Detection and Feature Fusion for Multiscale Remote Sensing Object Detection

Abstract

Multiscale object detection in remote sensing imagery poses significant challenges, including substantial variations in object size, diverse orientations, and interference from complex backgrounds. To address these issues, we propose a scale-aware detection and feature fusion network (SADFF-Net), a novel detection framework that incorporates a Multiscale contextual attention fusion (MCAF) module to enhance information exchange between feature layers and suppress irrelevant feature interference. In addition, SADFF-Net employs an adaptive spatial feature fusion (ASFF) module to improve semantic consistency across feature layers by assigning spatial weights at multiple scales. To enhance adaptability to scale variations, the regression head integrates a deformable convolution. In contrast, the classification head utilizes depth-wise separable convolutions to significantly reduce computational complexity without compromising detection accuracy. Extensive experiments on the DOTAv1 and DIOR_R datasets demonstrate that SADFF-Net outperforms current state-of-the-art methods in Multiscale object detection.