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Acta Armamentarii ›› 2022, Vol. 43 ›› Issue (S1): 146-154.doi: 10.12382/bgxb.2022.A013

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UAV Detection Method Based on Domestic Embedded Intelligent Computing Platform

CUI Lingfei, GUO Yonghong, XIU Quanfa, SHI Chao, ZHANG Shuoyang   

  1. (Institute of Computer Application Technology,NORINCO Group,Beijing 100089,China)
  • Online:2022-06-28

Abstract: A UAV detection method based on a domestic embedded intelligent computing platform is proposed to meet the actual requirements of anti-unmanned aerial vehicle(UAV) reconnaissance on the land battlefield. For the problem that UAV is small in size and not easy to be detected in the battlefield environment,the detection method is to use infrared and visible light images and video streams inputs for target detection. For the limited computing power and storage capacity of embedded platform,a lightweight deep neural network is built,and the feature extraction network in single shot multi-box detector(SSD) is replaced with MobileNet for model compression. The embedded platform Bitmain SE5 intelligent computing box is selected for verification, and the model conversion and transplantation are achieved. The experimental result shows that the proposed UAV detection method based on the lightweight deep neural network MobileNet-SSD can accurately determine the type of targets on the embedded intelligent computing platform, and the mean recognition accuracy and frame rate are basically same with those running in the development environment. It fully shows that the detection method can meet the requirements of the real-time and accuracy of UAV detection algorithm in the application environment in terms of speed and accuracy after being transplanted on the embedded intelligent computing platform.

Key words: UAVdetection, embeddedintelligentcomputingplatform, anti-UAV, lightweightdeepneuralnetwork

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