基于计算机视觉技术的无人机检测跟踪方法

Unmanned Aerial Vehicle Detection and Tracking Method Based on Computer Vision Technology

针对无人机因目标较小而难以检测、检测速度慢、难于跟踪等问题,提出一种基于目标检测YOLOv5s算法和目标跟踪DeepSORT算法的无人机检测跟踪方法;采用自采数据集和公开数据集构建无人机检测数据集,使用针对小目标的数据增强方法以扩充数据集多样性;选择合适的YOLOv5算法模型实现无人机目标的精准、快速检测,引入基于批标准化层的模型剪枝方法进一步提高模型检测速度;利用DeepSORT算法实现无人机目标的实时追踪;通过对比YOLOv3、 YOLOv4、 Fast R-CNN以及改进前的YOLOv5算法,验证了本文方法在无人机检测方面的性能。结果表明:提出的无人机检测跟踪方法的全类平均精度达到0.947,每秒浮点运算次数达到2.93×10~9,在无人机检测的精度和速度方面均具有优势。

To solve the problems of difficult detection,slow detection speed and difficult tracking for unmanned aerial vehicles(UAVs)due to small target,a UAVs detection and tracking method based on YOLOv5s algorithm and Deep-SORT algorithm was proposed.Self-collected data set and open data set were used to construct UAVs detection data set,and data enhancement method for small targets was used to expand the diversity of data set.The appropriate YOLOv5 al-gorithm model was selected to achieve accurate and fast detection of UAVs targets,and the model pruning method based on batch normalization layer was introduced to further improve the model detection speed.DeepSORT algorithm was ap-plied to realize the real-time tracking of UAVs targets.By comparing YOLOv3 YOLOv4,Fast R-CNN,and the unim-proved YOLOv5 algorithm have verified the performance of the proposed method in drone detection.The results show that the whole class average accuracy of the proposed UAVs detection and tracking method reaches 0.947,and the number of floating-point operations reaches 2.93×109 times per second,which has the advantages in detection accuracy and detec-tion speed of UAVs detection.