Laser & Optoelectronics Progress, Volume. 58, Issue 12, 1210029(2021)

Deep Multi-Scale Feature Fusion Target Detection Algorithm Based on Deep Learning

Xin Liu... Siyi Chen***, Xiaolong Chen** and Xinhao Du* |Show fewer author(s)
Author Affiliations
  • School of Automation and Electronic Information, Xiangtan University, Xiangtan, Hunan 411105, China
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    Figures & Tables(13)
    Algorithm framework of SSD
    Algorithm framework of DMSFFD
    Feature fusion structure. (a) First feature fusion; (b) second feature fusion
    Test result of occluded object by each algorithm. (a) SSD;(b) DMSFFD
    Positioning result of target by each algorithm. (a) SSD;(b) DMSFFD
    Detection result of small target by each algorithm. (a) SSD;(b) DMSFFD
    Detection result of multiple occluded objects by each algorithm. (a) SSD;(b) DMSFFD
    • Table 1. Feature map details

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      Table 1. Feature map details

      SSDDMSFFDSize /( pixel×pixel)
      Feature mapDimentionFeature mapDimention
      Conv4_3512F1conv25638×38
      Conv71024F2conv25619×19
      Conv8_2512F3conv25610×10
      Conv9_2256F4conv2565×5
      Conv10_2256F5conv2563×3
      Conv11_2256F6conv2561×1
    • Table 2. Test results on VOC2007 dataset unit:%

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      Table 2. Test results on VOC2007 dataset unit:%

      ImageDSSDSSDFasterDMSFFD
      Aero89.988.476.590.7
      Bike87.986.079.089.7
      Bird85.578.970.990.3
      Boat78.475.865.588.0
      Bottle53.948.852.170.3
      Bus88.686.883.190.7
      Car86.284.184.790.0
      Cat91.990.986.490.9
      Chair71.169.152.087.1
      Cow89.588.081.990.9
      Table78.778.465.789.0
      Dog91.390.584.890.8
      Horse89.689.084.690.6
      Motor88.486.877.590.6
      Person79.276.276.784.5
      Plant61.857.038.881.7
      Sheep78.072.773.682.7
      Sofa89.988.373.993.9
      Train93.292.083.097.0
      TV84.483.472.690.6
    • Table 3. mAP comparison of algorithms on VOC2007 test set

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      Table 3. mAP comparison of algorithms on VOC2007 test set

      AlgorithmDSSDSSDFasterDMSFFD
      mAP/%81.480.573.288.5
    • Table 4. Detection speed comparison frame/s

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      Table 4. Detection speed comparison frame/s

      AlgorithmDSSDSSDDMSFFD
      Detection time9.56338
    • Table 5. Test results on VOC2012 test set %

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      Table 5. Test results on VOC2012 test set %

      ImageDSSDSSDFasterDMSFFD
      Aero87.387.084.990.7
      Bike84.383.879.890.3
      Bird79.478.874.390.0
      Boat69.668.053.984.0
      Bottle56.855.449.871.9
      Bus86.784.077.590.6
      Car76.575.075.984.1
      Cat92.990.888.590.9
      Chair69.565.045.683.9
      Cow81.379.777.190.0
      Table74.372.655.385.1
      Dog91.590.386.990.9
      Horse88.688.281.790.7
      Motor88.686.880.990.5
      Person82.179.579.686.3
      Plant60.359.440.178.5
      Sheep79.677.872.687.3
      Sofa79.779.560.990.1
      Train88.288.181.290.8
      TV79.978.861.590.6
    • Table 6. mAP comparison of algorithms on VOC2012 test set unit:%

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      Table 6. mAP comparison of algorithms on VOC2012 test set unit:%

      AlgorithmDSSDSSDFasterDMSFFD
      mAP79.978.470.487.4
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    Xin Liu, Siyi Chen, Xiaolong Chen, Xinhao Du. Deep Multi-Scale Feature Fusion Target Detection Algorithm Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1210029

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    Paper Information

    Category: Image Processing

    Received: Jul. 14, 2020

    Accepted: Oct. 12, 2020

    Published Online: Jun. 22, 2021

    The Author Email: Chen Siyi (651972992@qq.com), Chen Xiaolong (540536315@qq.com), Du Xinhao (973151308@qq.com)

    DOI:10.3788/LOP202158.1210029

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