基于YOLOv4与KCF的无人机飞行障碍精准识别与实时跟踪研究

Precise Recognition and Real-time Tracking of UAV obstacle based on YOLOv4 and KCF

低空飞行的无人机,时常面临飞机、鸟类、行人、汽车、交通灯等障碍物的碰撞威胁。如何准确地检测出这些飞行障碍,并在规划飞行路径(RoutePlan)时躲避它们,已经成为无人机研究领域中的一个重要课题。近年来,基于深度学习(DeepLearning)的图像识别技术(ImageRecognition)在生物特征识别、物体分类等领域展现了强大的分类检测效能,这也为无人机飞行障碍检测提供了可能的解决思路及其实现途径。本文专注于无人机飞行障碍的检测与跟踪(DetectandTrack)环节,基于YOLOv4目标检测与KCF目标跟踪,对低空常见的典型障碍物进行精准识别与实时跟踪(PreciseRecognitionandReal-timeTracking);探索了高准确率的YOLOv4目标检测(ObjectDetection)与高运行效率的KCF目标跟踪(ObjectTracking)之间的最佳平衡点,从而兼顾了无人机障碍物检测的准确率与实时性,具有较强的理论探索意义和工程应用价值。

Flying UAV(Unmanned Aerial Vehicle)often threatens by plane,birds,pedestrian,car,traffic light and so on.How to detect those obsta⁃cles,and avoid them while planning flight route,has become an important research topic of UAV.Image Recognition based on Deep-Learn⁃ing has shown its ability to classify in domains like Biometrics Identification and Object Classification.Apply Image Recognition technolo⁃gy on UAV obstacle detection is also possible and achievable.This paper focuses on UAV obstacle detection and obstacle tracking.Based on YOLOv4 object detection and KCF object tracking,we carried out precise obstacle detection and real-time tracking on typical obsta⁃cles.We explored the optimal balance between high-accurate YOLOv4 object detection and high-efficient KCF object tracking,thus ensur⁃ing the accuracy and real-time performance of UAV obstacle detection simultaneously.