关键词: 无人机目标检测, 反无人机技术, 多尺度特征, 小目标检测

Abstract: In Unmanned Aerial Vehicles (UAVs) monitoring,small UAVs face significant challenges in target detection in complex environments due to their small size,diverse flight attitudes,and tendency to produce motion blur.To address this,a high-precision detection model for small UVAs,UAV-Net,was proposed.This model integrated four core improvement mechanisms: First,Edge Aware Convolution (EAC) module was designed to enhance the model's ability to extract edge features;Second,the model's neck was optimized and a Multi-scale Feature Focusing (MFF) network was proposed to strengthen feature interaction and fusion; Third,Dynamic Task Decomposition and Alignment (DTDA) detection heads were utilized to optimize the collaboration mode for localization and classification tasks;Finally,the Generalized Intersection over Union (GIoU) loss function was adopted to improve the regression of bounding boxes.Experimental results on the public drone dataset DUT Anti-UAV show that the proposed UAV-Net model achieves improvements of 1.5,3.9,3.1,and 2.6 percentage points over the baseline model in terms of precision,recall,mAP50,and mAP50-95,respectively,thereby validating the effectiveness of UAV-Net as a high-performance anti-drone object detection model for real-world scenarios.Experimental results on the Det-Fly dataset and Visdrone2019 dataset also demonstrate the generalization and applicability of UAV-Net in both air-to-air anti-UAV target detection tasks and small target detection tasks.

Key words: Unmanned aerial vehicle target detection, Anti-UAV technology, Multi-scale features, Small target detection