Low-altitude UAV detection and tracking algorithms in complex backgrounds
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摘要: 提出一种基于YOLOv5与CSRT算法优化的实时长跟踪方法，实现了对无人机在净空、城市、森林等场景的稳定跟踪。针对跟踪的不同阶段建立不同分辨率的两个捕获网络，分别对两个网络进行小目标检测优化和性能优化，并根据无人机数据集特点对其进行正负样本的添加以实现数据增强。然后，对CSRT算法使用GPU进行优化并结合特征点提取构建了低空无人机检测与跟踪模型。最后，将算法使用Tensorrt部署后在自建数据集上进行实验，实验结果表明，所提方法在RTX 2080Ti上实现了400FPS的跟踪性能，在 NVIDIA Jetson NX上实现了70FPS的性能。在实际外场实验中也实现了稳定的长时间跟踪。Abstract: With the frequent appearance of UAVs in several recent local wars and armed conflicts, the study of UAV detection and tracking technology has become a research hotspot in imagery and other fields. Due to the characteristics of low altitude UAV targets such as large mobility, small size, low contrast and complex background, their capture and tracking is a major challenge in the field of photoelectric detection. To address these difficulties, this paper proposes a real-time long tracking method based on YOLOv5 and CSRT algorithm optimization to achieve stable tracking of UAVs in clear sky, urban and forest scenes. First, two capture networks with different resolutions are established for different stages of tracking, and the two networks are optimized for small target detection and performance optimization respectively, and positive and negative samples are added to the UAV data set according to its characteristics to achieve data enhancement. Then, the CSRT algorithm is optimized using GPU and combined with feature point extraction to construct a low-altitude UAV detection and tracking model. Finally, the algorithm is deployed using Tensorrt and experimented on a self-built dataset. The experimental results show that the proposed method achieves a tracking performance of 400FPS on RTX 2080Ti and 70FPS on NVIDIA Jetson NX. Stable long-time tracking is also achieved in real field experiments.
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Key words:
- drone detection /
- real-time tracking /
- complex background /
- maneuvering target /
- drone countermeasures
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图 9 结合检测跟踪算法与特征点提取的无人机检测跟踪模型框图
Figure 9. Block diagram of UAV detection and tracking model combining detection and tracking algorithm and feature point extraction
图 11 优化后算法跟踪效果可视化
Figure 11. Visualization of algorithm tracking effect after optimization
表 1 不同检测算法性能对比
Table 1. Performance comparison of different detection algorithms
algorithm size of input image/pixel δAP50/% δAP75/% detection speed/(frames·s−1) YOLOv5 1024×1024 86.2 57.8 35 UavYOLO-B 1024×1024 89.1 59.2 25 UavYOLO-S 320×320 88.3 58.5 200 
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表 2 不同检测算法性能对比
Table 2. Performance comparison of different detection algorithms
algorithm capture accuracy/% miss distance/pixel tracking speed/(frames·s−1) YOLOv5 87.2 10.6 35 UavYOLO-B+UavYOLO-S 93.2 5.4 25 UavYOLO-B+UavYOLO-S+CSRT 97.8 4.8 75 UavYOLO-B+UavYOLO-S+CSRT+ KeyPoints 97.8 1.5 75
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