YOLO-FeatSup: Enhancing Small-Object Detection in Remote Sensing Images With GLSA and ScalSeq Modules Based on YOLOv11

Small-object detection in remote sensing remains challenging due to low-resolution, weak cues, and complex backgrounds. Although semantic-context enhancement (SCE) methods leverage spatial priors effectively, their effectiveness is often limited by the stage, which contextual features are integrated. When introduced in the backbone, contextual information is subsequently degraded by the neck’s repeated resampling and fusion operations. Conversely, integrating context modeling mechanisms between the neck and the detection head is also suboptimal, as the features have already undergone multiscale mixing and spatial transformations, resulting in weakened semantic representation. To address this issue, we propose YOLO-feature supplement (FeatSup), a context-preserving detector built upon YOLOv11, which supplements the detection head with preserved semantic features via a dedicated bypass path. Specifically, a global−local spatial aggregation (GLSA) module is integrated in the backbone to capture high-fidelity semantic features before scale mixing, while the scale sequence (ScalSeq) fusion module performs effective multiscale feature fusion and delivers the enriched features directly to the small-object detection branch, thereby avoiding the degradation typically caused by repeated resampling and cross-scale operations in the feature aggregation process. Experiments on six public datasets demonstrate that YOLO-FeatSup achieves superior small-object detection accuracy with reduced computational cost, offering an efficient solution for remote sensing applications.