SAFE-Det: Scale-Adaptive Feature Enhancement for Remote Sensing Small-Object Detection - Ke,Z; Xu,S; Wang,G; Zhang,Y; Yang,G (IEEE Geoscience and Remote Sensing Letters, 2026)
Due to the minuscule size of objects and the presence of background noise in remote sensing images, the object detection in such images remains a challenging task. In an effort to enhance the detection accuracy of small objects, this letter proposes a novel scale-adaptive feature enhancement detection (SAFE-Det) framework named SAFE-Det. This framework is specifically designed for small-object detection in remote sensing images. SAFE-Det utilizes the residual connections and a multiscale parallel attention downsample (MPAD) module to dynamically preserve the features of small objects, thereby reducing the information loss. Moreover, it incorporates an adaptive perception core (APC) model that combines a triple-branch pyramid attention (TBPA) and microtarget augmentation (MTA). This module aims to highlight small objects while suppressing the background noise. In addition, SAFE-Det features a specialized small-object detection head that leverages high-resolution shallow features, thus overcoming the limitations of conventional methods. Experimental results on the VisDrone2019 and VEDAI datasets indicate that SAFE-Det achieves an mAP@0.5 of 41.3% and 73.0%, respectively. These results represent respective improvements of 7.9% and 6.7% over baseline models. Codes are available at: https://github.com/xjh1017/Reunion