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Journal of Projectiles, Rockets, Missiles and Guidance >

2022 , Vol. 42 >Issue 5: 32 - 37

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2022.05.007

Target Detection System and Realization of UAV Image Based on Improved YOLO_v3-SPP

  • LIU Yongfeng 1 ,
  • SHEN Yan’an 2 ,
  • WEI Zhe 1 ,
  • LI Congli 1
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  • 1 Department of Weapon Engineering, Army Academy of Artillery and Air Defense, Hefei 230031, China
  • 2 Department of UAV Application, Army Academy of Artillery and Air Defense, Hefei 230031, China

Received date: 2021-09-16

  Online published: 2025-01-16

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Abstract

Aiming at the problems of low detection and high false detection of existing UAV image target detection algorithms, the method of intelligent target detection is carried out. Based on YOLO_v3-SPP network, more scales of feature fusion are performed on the network structure, and the information of the third and fourth convolution layers in DarkNet-53 is down sampled and sent to the network for fusion, which can improve the accuracy of multi-scale target detection; anomaly detection network is introduced to make a second judgment on the target, which can eliminate the misdetected samples and effectively reduce the misjudgment rate of target detection. Experiments on public data sets and self-built military target data sets show that the map value is improved by 4%, which shows that the algorithm improves the problems of missed detection and misdetection in the application of existing algorithms to reconnaissance image. Finally, the algorithm model is transplanted to the hardware platform, and the system detection speed is less than 40 ms, which has a good detection effect.

Key words： UAV image; target detection; feature fusion; anomaly detection; YOLO_v3-SPP

Cite this article

LIU Yongfeng , SHEN Yan’an , WEI Zhe , LI Congli . Target Detection System and Realization of UAV Image Based on Improved YOLO_v3-SPP[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2022 , 42(5) : 32 -37 . DOI: 10.15892/j.cnki.djzdxb.2022.05.007

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