Detection method for a dynamic small target using the improved YOLOv3

被引：0
|
作者：
Cui Y. [1 ,2 ]
Wang Y. [1 ]
Hu J. [1 ,2 ]
机构：
[1] School of Network and Information Security, Xidian University, Xi'an
[2] Network Behavior Research Center, Xidian University, Xi'an
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2020年 / 47卷 / 03期
关键词：
Anti-drone; Complex background; Convolutional neural network; Real-time detection; Super resolution;
D O I：
10.19665/j.issn1001-2400.2020.03.001
中图分类号：
学科分类号：
摘要：
The visual characteristics of low-altitude drones are less obvious and the scale changes during the detection process. Traditional detection methods are susceptible to interference during detection, and most of those methods cannot work quickly and robustly. To solve this problem, a real-time drone detection algorithm combined with the improved YOLOv3 model and the super resolution method is proposed in this paper. First, frame difference is used to propose the candidate area, and the super-resolution method is used to strengthen the details. Then the dimensional clustering algorithm is used to regenerate the anchors for the model, and the model is slightly adjusted. Finally, we use the improved YOLOv3 to scan both the whole frame and the processed candidate area so as to detect the drones. The frame relationship is also used to implement tracking of drones by real-time detection. With GPU (GTX 1070Ti) acceleration, the method works at a speed of about 20FPS and has an accuracy rate of 96.8% and a recall rate of 95.6%. The results prove that the method can detect drones in different complex backgrounds with a considerable effective detection distance. Compared with the traditional method or normal machine learning method, our method is of a certain theoretical and practical value. © 2020, The Editorial Board of Journal of Xidian University. All right reserved.
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页码：1 / 7
页数：6
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