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Acta Armamentarii ›› 2021, Vol. 42 ›› Issue (12): 2664-2674.doi: 10.3969/j.issn.1000-1093.2021.12.014

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A Lightweight Target Detection Algorithm Based on the Improved Faster-RCNN

MA Yuehong, KONG Mengyao   

  1. (College of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China)
  • Online:2022-01-15

Abstract: The target detection algorithms based on deep learning has become the mainstream of target detection in synthetic aperture radar images. Deep network algorithm often has a large number of parameters and don't run fast enough to meet real-time requirements,making it difficult to deploy on resource-constrained devices such as mobile terminal. Considering the requirements of real-time performance and portability of the model,Faster-RCNN for the two-stage target detection algorithm was improved to compare the influence of different improved methods on the speed and accuracy of algorithm.The lightweight model was optimized in combination with the characteristics of synthetic aperture radar (SAR) images,and finally compared with the single shot multibox detector for one-stage target detection algorithm.The experimental results show that the speed of the improved lightweight model is greatly improved while maintaining the original accuracy level,which can effectively meet the real-time requirements of SAR image target detection.

Key words: targetdetection, fasterregion-basedconvolutionalneuralnetwork, syntheticapertureradar, lightweightalgorithm, real-time

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