• Laser & Optoelectronics Progress
  • Vol. 59, Issue 18, 1815003 (2022)
Weigang Li*, Chao Yang, Lin Jiang, and Yuntao Zhao
Author Affiliations
  • Engineering Research Center for Metallurgical Automation and Measurement Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei , China
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    DOI: 10.3788/LOP202259.1815003 Cite this Article Set citation alerts
    Weigang Li, Chao Yang, Lin Jiang, Yuntao Zhao. Indoor Scene Object Detection Based on Improved YOLOv4 Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815003 Copy Citation Text show less
    Diagram of YOLOv4 network structure
    Fig. 1. Diagram of YOLOv4 network structure
    Prediction box of YOLOv4 algorithm in 19×19 cells
    Fig. 2. Prediction box of YOLOv4 algorithm in 19×19 cells
    Diagram of improved YOLOv4 network structure
    Fig. 3. Diagram of improved YOLOv4 network structure
    CSPNet structured in SPP module
    Fig. 4. CSPNet structured in SPP module
    CSPNet structured in continuous convolution module
    Fig. 5. CSPNet structured in continuous convolution module
    Depthwise separable convolution
    Fig. 6. Depthwise separable convolution
    Scatter plot of the size distribution of sample ground truth box and prior box
    Fig. 7. Scatter plot of the size distribution of sample ground truth box and prior box
    Accuracy comparison between K-means algorithm and K-means++ algorithm under original YOLOv4
    Fig. 8. Accuracy comparison between K-means algorithm and K-means++ algorithm under original YOLOv4
    Comparison of detection results before and after algorithm improvement
    Fig. 9. Comparison of detection results before and after algorithm improvement
    Comparison of detection results on each class before and after algorithm improvement
    Fig. 10. Comparison of detection results on each class before and after algorithm improvement
    MethodNeck moduleParameters /107mAP /%Speed /(frame·s-1
    1SPP+PAN5.3581.066.1
    2SPP+CSPPAN4.6581.866.5
    3CSPSPP+PAN5.9582.663.6
    4CSPSPP+CSPPAN5.2583.662.3
    Table 1. Performance results of the neck network integrated into the CSPNet structure
    MethodBackboneNeckParameters/107mAP /%Speed /(frame·s-1
    1CSPDarknet53PAN5.9582.663.5
    2CSPDarknet53CSPPAN5.2583.662.3
    3DS-CSPDarknet53PAN4.4181.967.0
    4DS-CSPDarknet53CSPPAN3.8983.268.0
    5CSPDarknet53DS-PAN4.7282.068.7
    6CSPDarknet53DS-CSPPAN4.6082.965.8
    7DS-CSPDarknet53DS-PAN3.2881.972.9
    8DS-CSPDarknet53DS-CSPPAN3.2483.072.1
    Table 2. Impact of depthwise separable convolution on network performance
    Improvement strategyWeight size /MBmAP /%Speed /(frame·s-1
    K-means++CSP-NeckDS
    10279.866.1
    5279.378.6
    10181.262.4
    6580.671.9
    10281.066.1
    5280.878.1
    10183.662.3
    6583.072.1
    Table 3. Performance comparison of different improvements
    ModelWeight size /MBmAP /%Speed /(frame·s-1
    Faster-RCNN-resnet5031579.021.3
    SSD-resnet5010878.240.5
    YOLOv311875.260.8
    YOLOv410279.866.1
    YOLOv59481.572.5
    Improved YOLOv46583.072.1
    Table 4. Performance comparison of different detection algorithms
    Weigang Li, Chao Yang, Lin Jiang, Yuntao Zhao. Indoor Scene Object Detection Based on Improved YOLOv4 Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815003
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