DeCenter: Density-Center Guided Perception Enhancement for UAV Object Detection

Uncrewed aerial vehicle (UAV) object detection is essential for applications such as surveillance, agriculture, and disaster response. However, UAV imagery often contains small, dense, and occluded objects, posing challenges for existing methods. To address these challenges, we propose DeCenter, a novel Density-Center Guided Perception Enhancement framework for UAV object detection. DeCenter is composed of two key modules that jointly enhance the perception of small and crowded objects. First, the Density-Guided Object Center Heatmap Generator (DOCHG) adaptively generates Gaussian kernel-based heatmaps according to local density information, guiding the model to emphasize central neighborhoods of objects in crowded regions. This mechanism reduces overlaps between adjacent instances and alleviates missed detections under occlusion. Second, the Density-Center Feature Enhancement module (DCFE) integrates complementary cues from density features and object centers, adaptively balancing region-level object distribution with fine-grained localization. By fusing these signals, DCFE enhances the quality of feature representations, making them more discriminative for dense small objects while suppressing background noise. Experimental results on VisDrone and UAVDT datasets show that DeCenter achieves competitive overall accuracy with clear improvements in detecting dense small objects, offering an effective solution for UAV object detection. The code will be available at https://github.com/bluuzzz/decenter