Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (9): 2885-2892.DOI: 10.11772/j.issn.1001-9081.2021071146

• Multimedia computing and computer simulation • Previous Articles    

Multi-UAV real-time tracking algorithm based on improved PP-YOLO and Deep-SORT

Jun MA1,2(), Zhen YAO1, Cuifeng XU1,2, Shouhong CHEN1,2   

  1. 1.School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin Guangxi 541004,China
    2.Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (Guilin University of Electronic Technology),Guilin Guangxi 541004,China
  • Received:2021-07-02 Revised:2021-09-14 Accepted:2021-09-15 Online:2021-09-24 Published:2022-09-10
  • Contact: Jun MA
  • About author:YAO Zhen, born in 1996, M. S. candidate. His research interests include deep learning, target detection, target tracking.
    XU Cuifeng, born in 1977, M. S., senior experimentalist. Her research interests include signal processing, digital image processing.
    CHEN Shouhong, born in 1981, M. S., senior experimentalist. His research interests include neural network, machine learning, computer-aided testing.
  • Supported by:
    National Natural Science Foundation of China(61671008);Innovation Project of Graduate Education of Guilin University of Electronic Technology(2020YCXS095)


马峻1,2(), 姚震1, 徐翠锋1,2, 陈寿宏1,2   

  1. 1.桂林电子科技大学 电子工程与自动化学院,广西 桂林 541004
    2.广西自动检测技术与仪器重点实验室(桂林电子科技大学),广西 桂林 541004
  • 通讯作者: 马峻
  • 作者简介:姚震(1996—),男,陕西商南人,硕士研究生,主要研究方向:深度学习、目标检测、目标跟踪;
  • 基金资助:


The target size of the Unmanned Aerial Vehicle (UAV) is small, and the characteristics among multiple UAVs are not obvious. At the same time, the interference of birds and flying insects brings a huge challenge to the accurate detection and stable tracking of the UAV targets. Aiming at the problem of poor detection performance and unstable tracking of small target UAVs by using traditional target detection algorithms, a real-time tracking algorithm for multiple UAVs based on improved PaddlePaddle-YOLO (PP-YOLO) and Simple Online and Realtime Tracking with a Deep association metric (Deep-SORT) was proposed. Firstly, the squeeze-excitation module was integrated into PP-YOLO detection algorithm to achieve feature extraction and detection of UAV targets. Secondly, the Mish activation function was introduced into ResNet50-vd structure to solve the problem of vanishing gradient in the back propagation process and further improve the detection precision. Thirdly, Deep-SORT algorithm was used to track UAV targets in real time, and the backbone network that extracts appearance features was replaced with ResNet50, thereby improving the original network’s weak perceptual ability of small appearances. Finally, the loss function Margin Loss was introduced, which not only improved the class separability, but also strengthened the tightness within the class and the difference between classes. Experimental results show that the detection mean Average Precision (mAP) of the proposed algorithm is increased by 2.27 percentage points compared to that of the original PP-YOLO algorithm, and the tracking accuracy of the proposed algorithm is increased by 4.5 percentage points compared to that of the original Deep-SORT algorithm. The proposed algorithm has a tracking accuracy of 91.6%, can track multiple UAV targets within 600 m in real time, and effectively solves the problem of "frame loss" in the tracking process.

Key words: Unmanned Aerial Vehicle (UAV) detection, real-time tracking, squeeze-excitation module, Mish activation function, Margin Loss


无人机(UAV)目标尺寸较小,多架无人机之间特征也不明显,且鸟类和飞虫的干扰给无人机目标的准确检测和稳定跟踪带来了巨大挑战。针对传统目标检测算法对小目标无人机检测性能差、跟踪不稳定的问题,提出一种基于改进PP-YOLO和Deep-SORT的多无人机实时跟踪算法。首先,将压缩-激励模块融入PP-YOLO检测算法中,以实现对无人机目标的特征提取和检测;其次,在ResNet50-vd结构中引入Mish激活函数,以解决反向传播过程中的梯度消失问题,并进一步提升检测精度;然后,采用Deep-SORT算法来实时跟踪无人机目标,并将提取外观特征的主干网络更换为ResNet50,从而改善原有网络对微小外观感知能力弱的状况;最后,引入损失函数Margin Loss,既提高了类别可分性,又加强了类内紧度和类间差异。实验结果表明,所提算法的检测平均精度均值(mAP)相比原始PP-YOLO算法提升了2.27个百分点,跟踪准确性相对于原始Deep-SORT算法提升了4.5个百分点。所提算法的跟踪准确性可达91.6%,能够实时跟踪600 m以内多架无人机目标,有效解决了跟踪过程中的“丢帧”问题。

关键词: 无人机检测, 实时跟踪, 压缩-激励模块, Mish激活函数, Margin Loss

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