Laser & Optoelectronics Progress, Volume. 57, Issue 10, 101010(2020)

Multi-Target Recognition Method Based on Improved YOLOv2 Model

Xun Li1, Binbin Shi1、*, Yang Liu2, Lei Zhang1, and Xiaohua Wang1
Author Affiliations
  • 1School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • 2Xi'an Metrological Technology Research Institute, Xi'an, Shaanxi 710068, China
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    Figures & Tables(13)
    Target position
    Test results of different models. (a) Model 1; (b) model 2; (c) model 3; (d) model 4; (e) model 5; (f) model 6
    Frame of YOLOv2-voc_mul model
    Model data sample. (a) Car; (b) van; (c) bus; (d) truck
    Loss graphs of different models. (a) YOLOv2; (b) YOLOv2-voc; (c) YOLOv3; (d) YOLOv2-voc_mul
    Verification results of different models. (a) YOLOv2; (b) YOLOv2-voc; (c) YOLOv3; (d) YOLOv2-voc_mul
    Test results at different number of iterations. (a) 60000; (b) 70000
    Added model samples
    Test results of different models. (a) YOLOv2 model; (b) YOLOv2-voc model; (c) YOLOv3 model; (d) YOLOv2-voc_mul model
    • Table 1. Network framework

      View table

      Table 1. Network framework

      ModelLearning rateLayer number of network structureActivation function
      ConvolutionlayerMaximum pooling layer+average pooling layerBN layer
      Model 10.001235+02222 leaky+1 linear
      Model 20.0001235+02022 leaky+1 linear
      Model 30.01235+02222 leaky+1 linear
      Model 40.001205+12219 leaky+1 linear
      Model 50.001205+12219 leaky+1 ReLU
      Model 60.001205+02019 leaky+1 linear
    • Table 2. Test results

      View table

      Table 2. Test results

      ModelNtotalCcorrectPproposalPprecision /%Rrecall /%F1 /%
      YOLOv215214615196.6996.0596.36
      YOLOv2-voc15214414698.6394.7496.64
      YOLOv31528515056.6755.9269.68
      YOLOv2-voc_mul15214514699.2095.3997.26
    • Table 4. Comparison of mAP value in different models

      View table

      Table 4. Comparison of mAP value in different models

      ModelmAP /%
      TruckBusVanCar
      YOLOv286.4584.1682.3786.88
      YOLOv2-voc87.6185.2283.0287.31
      YOLOv383.3581.8377.9683.92
      YOLOv2-voc_mul88.7288.5686.6489.03
    • Table 5. Average accuracy of vehicle identification in the YOLOv2-voc_mul model

      View table

      Table 5. Average accuracy of vehicle identification in the YOLOv2-voc_mul model

      TypeAccuracy /%Averageaccuracy /%
      TruckBusVanCar
      Simple target92.1491.8990.0894.7292.21
      Multiple target89.0188.7688.2091.7189.44
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    Xun Li, Binbin Shi, Yang Liu, Lei Zhang, Xiaohua Wang. Multi-Target Recognition Method Based on Improved YOLOv2 Model[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101010

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    Paper Information

    Category: Image Processing

    Received: Aug. 28, 2019

    Accepted: Oct. 18, 2019

    Published Online: May. 8, 2020

    The Author Email: Binbin Shi (734931099@qq.com)

    DOI:10.3788/LOP57.101010

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