基于多阶运动参量的四旋翼无人机识别方法

以小型多轴无人机为代表的“低慢小”目标, 通常难以被常规手段探测, 而此类目标又会严重威胁某些重要设施. 因此对该类目标的识别已经成为一个亟待解决的重要问题. 本文基于目标运动特征, 提出了一种无人机目标识别方法, 并揭示了二阶运动参量以及重力方向运动参量是无人机识别过程中的关键参数. 该方法首先提取候选目标的多阶运动参量, 建立梯度提升树(Gradient boosting decision tree, GBDT)和门控制循环单元(Gate recurrent unit, GRU)记忆神经网络分别完成短时和长期识别, 然后融合表观特征识别结果得到最终判别结果. 此外, 本文还建立了一个综合多尺度无人机数据集(Multi-scale UAV dataset, MUD), 本文所提出的方法在该数据集上相对于传统基于运动特征的方法, 其识别精度(Average precision, AP)提升103%, 融合方法提升26%.

Drone Detection Based on Multi-order Kinematic Parameters

Due to the features of low, slow and small aircraft, such as quadrotors, it is a challenging and urgent problem to detect UAVs (Unmanned aerial vehicles) in the wild. Different from the past literatures directly using deep learning method, this paper exploits motion features by extracting multi-order kinematic parameters such as velocity, accelerate, angular velocity, angular velocity vectors and it is exposed that 2nd order and gravity direction motion parameters are key motion patterns for UAV detection. By building GBDT (Gradient boosting decision tree) and GRU (Gate recurrent unit) network, it comes out with a short-term and a long-term detection result, respectively. This recognition process integrates appearance detection result into motion detection result and obtains the final determination. The experimental results achieve state-of-the-art result, with a 103% increase on the precision index AP (Average precision) with respect to the previous work and a 26% increase for hybrid method.