基于位置约束与注意力的低空无人机障碍物检测方法  

A low-altitude UAV obstacle detection method based on position constraint and attention mechanism

作　　者：唐友军 缪存孝[1] 张贺[1] 李玉峰 叶文 TANG Youjun;MIAO Cunxiao;ZHANG He;LI Yufeng;YE Wen(School of Mechanical Engineering,University of Science and Technology Beijing,Beijing 100083,China;National Institute of Metrology,China,Beijing 100029,China)

机构地区：[1]北京科技大学机械工程学院,北京100083 [2]中国计量科学研究院,北京100029

出　　处：《北京航空航天大学学报》2025年第3期933-942,共10页Journal of Beijing University of Aeronautics and Astronautics

摘　　要：无人机(UAV)在低空领域广泛应用于电力巡检、搜救、侦察等任务,对飞行过程中的障碍物进行提前检测是完成既定任务的安全保障。为满足无人机低空飞行时对障碍物的检测精度要求及位置回归精度要求,提出一种基于位置约束与注意力改进的低空无人机障碍物检测方法。分析位置回归损失函数的不足并基于此提出分离尺度损失与融合方向约束的损失函数对回归过程进行优化;改进注意力机制CBAM提出双重注意力机制以强化特征抑制干扰,提高检测性能。实验结果表明:本文方法在mAP上提高了2.28%,在mAP@0.5:0.95上提高了2.7%,在检测精度和位置回归精度上都表现出了更好的低空障碍物检出性能。Unmanned aerial vehicles(UAV)are widely used in low-altitude areas for power inspection,search and rescue,reconnaissance,and other tasks.The detection of obstacles in advance during flight aims to ensure the completion of established tasks.In order to meet the requirements of obstacle detection accuracy and position regression accuracy of UAV flying at low altitudes,a low-altitude UAV obstacle detection method based on improved position constraints and attention mechanism was proposed.The deficiency of position regression loss function was analyzed.On this basis,the loss function of separation scale loss and fusion direction constraint was proposed to optimize the regression process.The improved attention mechanism CBAM proposed a dual attention mechanism to strengthen the feature suppression interference and improve the detection performance.The experimental results show that the proposed method improves by 2.28%on mAP and 2.7%on mAP@0.5:0.95,showing better detection performance of low-altitude obstacles in terms of both detection accuracy and position regression accuracy.

关 键 词：低空无人机 障碍物检测 位置回归损失函数 双重注意力机制 位置回归损失函数 

分 类 号：V249[航空宇航科学与技术—飞行器设计]