无人机滥用给低空范围带来巨大安全隐患, 非法入侵无人机目标的检测问题成为低空防御系统中重要的研究方向. 本文提出一种基于雷达、RGB相机等多传感器信息融合方法, 用于探测低空范围内小目标物体. 然后, 引入SSD (Single Shot multibox Detector)深度学习算法, 训练无人机目标检测模型, 对RGB相机捕获到画面中物体类别与位置进行预测. 通过搭建实验平台验证信息融合方法能够成功获得目标位置、速度以及外观形态等特征, 深度学习模型能够成功判断可疑目标的类别.
The abuse of Unmanned Aerial Vehicles (UAVs) brings great security risks to the low altitude area. Then the research on detection of UAVs’ illegal intrusion has become important for a low-altitude defense system. In this study, a multi-sensor information fusion technique based on radar and a RGB camera is designed to detect small objects in the low altitude range. After that, the Single Shot multibox Detector (SSD) for deep learning is introduced to train the UAV detection model and predict the category and location of objects captured by the RGB camera. An experimental platform is built to verify that the information fusion method can collect the location, speed, appearance of targets, and the deep learning model can determine the categories of suspicious targets.