基于多尺度融合方法的无人机对地车辆目标检测算法研究

由于飞行高度等原因,无人机图像在实际使用中目标尺寸普遍较小、特征信息不明显,使用现有的算法对其进行目标检测存在困难。因此,提出了基于多尺度融合的图像多目标检测方法,使用Faster R-CNN为基础框架,将不同层次的特征信息进行融合,再结合上下文信息,实现了对无人机图像小目标检测。使用VisDrone2019数据集对地面车辆进行目标检查,实验证明:无人机对地面车辆目标的检测达到了较好的结果,所使用算法的精度达到88%,与其它算法相比提升了3.8%以上。

Research on Unmanned Aerial Vehicle to Ground Vehicle Target Detection Algorithm Based on Multiscale Fusion Method

With the development of drone technology,the use of drone images for ground vehicle target recognition is of great significance both in rescue and disaster relief and in traffic management. However,in actual use,due to the flight height and other reasons,the target in the image is generally small in size and the feature information is not obvious. It is difficult to detect the target using existing algorithms. Therefore,an image multi-target detection method based on multi-scale fusion is proposed. Using Faster R-CNN as the basic framework,the feature information of different levels is fused,and the context information is combined to realize the detection of small targets in unmanned aerial vehicle images. The VisDrone dataset is used to perform ground inspection on ground vehicles. Experiments have shown that the detection of ground vehicle targets by drones has achieved good results. The accuracy of the algorithm used has reached 88%, which is an increase of 3.8% compared with other algorithms the above.