• Laser & Optoelectronics Progress
  • Vol. 57, Issue 12, 121013 (2020)
Likai Li1, Chihua Lu1、2, and Bin Zou1、2、*
Author Affiliations
  • 1Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, Hubei 430070, China
  • 2Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan, Hubei 430070, China
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    DOI: 10.3788/LOP57.121013 Cite this Article Set citation alerts
    Likai Li, Chihua Lu, Bin Zou. Research on Target Detection and Feasible Region Segmentation Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121013 Copy Citation Text show less
    Structure of ResNet
    Fig. 1. Structure of ResNet
    Network structure of joint method for image detection and segmentation
    Fig. 2. Network structure of joint method for image detection and segmentation
    Feature prediction maps of different scales; (a) 20×15; (b) 10×8
    Fig. 3. Feature prediction maps of different scales; (a) 20×15; (b) 10×8
    Prior box for SSD. (a) Image with ground truth box; (b) feature image with a size of 10×8
    Fig. 4. Prior box for SSD. (a) Image with ground truth box; (b) feature image with a size of 10×8
    Flow chart of multi-task joint method
    Fig. 5. Flow chart of multi-task joint method
    Graph of learning rate and loss function. (a) Learning rate; (b) loss function
    Fig. 6. Graph of learning rate and loss function. (a) Learning rate; (b) loss function
    Detection and segmentation of data in the joint method. (a) Scene A; (b) scene B
    Fig. 7. Detection and segmentation of data in the joint method. (a) Scene A; (b) scene B
    Actual effect figure of intelligent vehicle joint method. (a) Scene A; (b) scene B
    Fig. 8. Actual effect figure of intelligent vehicle joint method. (a) Scene A; (b) scene B
    LayerOutput sizeConv layer
    Pre-block160×1207×7,64
    Block180×601×13×31×1,64,64,256×3 Conv
    Block240×301×13×31×1,128,128,512×4 Conv
    Block320×151×13×31×1,256,256,1024×6 Conv
    Block410×81×13×31×1,512,512,2048×3 Conv
    Table 1. Structure diagram of ResNet-50 network
    Detection modelFeature networkmAP /%FPS /frameDataset
    Faster R-CNNVGG1689.237cityscapes
    Faster R-CNNResNet-10191.808cityscapes
    YoLo v2GooleNet83.4621cityscapes
    OursResNet-5090.1532mixed dataset
    Table 2. Detection ability of different methods
    Split modelMIoUFPS /frameDataset
    PSPNet76.62cityscapes
    ResNet-3886.25cityscapes
    DeepLab v377.88cityscapes
    Ours87.632mixed dataset
    Table 3. Segmentation ability of different methods
    Likai Li, Chihua Lu, Bin Zou. Research on Target Detection and Feasible Region Segmentation Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121013
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