HEdge-MamYOLO: High-Frequency Edge Information-Facilitated Cross-Domain Feature Interaction Mamba for Drone Images Small-Object Detection

Unmanned Aerial Vehicle (UAV) small object detection remains challenging due to extreme scale variation, dense spatial distribution, and frequent occlusion in aerial images. Small objects often occupy very limited pixel regions and exhibit ambiguous structural boundaries, making their discriminative features easily overwhelmed by background clutter. These challenges lead to unstable multi scale representation and degraded localization precision in existing detectors. To address these challenges, combining Mamba and YOLO, we propose the HEdge-MamYOLO, which consists of three components. First, we design Frequency-Mamba Collaborative High-Frequency Enhancement Module (FM-CHFEM) to extract high-frequency components of enhanced visible edges from occluded objects in the frequency domain and leverage Mamba global selective scanning combined with spatial similarity to unoccluded objects of the same type to achieve feature enhancement and recognition of occluded objects. Second, we propose the Dynamic Scale Feature Fusion Module (DSFFM), by covering large-scale objects and adaptively matching small-scale object features, achieves balanced capture of objects at different scales under FM-CHFEM-based feature extraction. Finally, we design the Lightweight Low-Level Feature Fusion Head (LLFFH) by integrating high-resolution low-level features with DSFFM’s scale-adaptive features, it sparsely processes feature maps via partial convolution. This approach preserves details of small objects, and reduces computational overhead. Extensive experiments on the VisDrone2019 and UAVDT aerial image datasets, with mAP50 metrics reaching 52.5%and 33.4% respectively, which is better than other SOTA models, demonstrate the effectiveness of our proposed method.