关键词: 无人机, 高斯-拉普拉斯算子, 超图特征处理, 特征融合, 小目标检测, DEIM

Abstract: Object detection from an Unmanned Aerial Vehicle (UAV) perspective faces severe challenges, including highly complex backgrounds, drastic scale variations, and a lack of discriminative features for small objects. To overcome the performance bottlenecks of existing models in complex airspace environments, a UAV-oriented object detection model, LHM-DEIM, based on the DEIM (DEtection TRansformer with Improved Matching)-DFINE architecture, was proposed. To address the issue of texture loss in small objects, a Laplacian of Gaussian (LoG) Stem was introduced for initial feature extraction, utilizing the Laplacian operator to strengthen the representation of high-frequency spatial features. To capture cross-scale global correlations, the model's neck was reconstructed into a Hypergraph Hybrid Encoding Neck (H-HEN). In this neck, a Spatial Scale Alignment (SSA) mechanism was implemented, using the P4 layer as the base scale and other layers as enhancement features within a hierarchical fusion strategy. Furthermore, a hypergraph modeling strategy was incorporated to significantly increase the correlation density of high-order features. Additionally, a MutualGuideFusion module was designed to effectively mitigate feature attenuation in deep networks through a bidirectional information excitation mechanism, achieving efficient integration of multi-scale features. Experimental results on the VisDrone2019 dataset demonstrate that the LHM-DEIM model increases the average precision at an IoU threshold of 0.5 (AP₀.₅) from the baseline value of 34.3% to 38.2%, representing an increase of 3.9 percentage points. Specifically, the average precision for small objects (APs) increases by 2.5 percentage points. Ablation and generalization experimental results further confirm that the model has strong robustness and practicality in drone scenarios. 

Key words: Unmanned Aerial Vehicle (UAV), Laplacian of Gaussian (LoG) operator, hypergraph feature processing, feature fusion, small object detection, DETR with Improved Matching (DEIM)