• Laser & Optoelectronics Progress
  • Vol. 56, Issue 5, 051004 (2019)
Meiju Liu and Bo Yun*
Author Affiliations
  • Information & Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, Liaoning 110168, China
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    DOI: 10.3788/LOP56.051004 Cite this Article Set citation alerts
    Meiju Liu, Bo Yun. Application of Deep Convolution Network Compression Algorithm in Weld Recognition[J]. Laser & Optoelectronics Progress, 2019, 56(5): 051004 Copy Citation Text show less
    Weld recognition system[9]
    Fig. 1. Weld recognition system[9]
    Schematic of convolution calculation
    Fig. 2. Schematic of convolution calculation
    Activation function images. (a) Sigmoid; (b) RELU
    Fig. 3. Activation function images. (a) Sigmoid; (b) RELU
    Image acquisition device
    Fig. 4. Image acquisition device
    Convolution network structure used in experiments
    Fig. 5. Convolution network structure used in experiments
    Making labels for weldment 1
    Fig. 6. Making labels for weldment 1
    Computing result
    Fig. 7. Computing result
    Recognition rate comparison among models
    Fig. 8. Recognition rate comparison among models
    Second weldment
    Fig. 9. Second weldment
    Label of weldment point
    Fig. 10. Label of weldment point
    Polygon diagram of mixed test results
    Fig. 11. Polygon diagram of mixed test results
    Type of layersParameter
    Convolution+activation function+poolingC64(3×3,S=1)+RELU+Pmax(2×2,S=2)
    Convolution+activation function+poolingC128(3×3,S=1)+RELU+Pmax(2×2,S=2)
    Convolution+activation function+poolingC256(3×3,S=1)+RELU+Pavg(2×2,S=2)
    Convolution+activation function+poolingC512(3×3,S=1)+RELU+Pavg(2×2,S=2)
    Convolution+activation function+poolingC512(3×3,S=1)+RELU+Pavg(2×2,S=2)
    Full connetion+activation function400 neutral units+RELU
    Full connetion+activation function400 neutral units+RELU
    Loss functionEuclidean distance
    Table 1. Network structure and hyper-parameter list
    Without compressionAfter compressionCompressionratioSpeed ratioLoss /%
    Maximum memoryrequirement /MBModelsize /MBMaximum memoryrequirement /MBModelsize /MB
    3855.31.12.3323.826.31.4
    Table 2. Model size and memory requirements
    MethodAccuracy /%
    GS94.3
    SURF92.4
    HC90.1
    LSE87.6
    GH93.3
    GXN97.5
    Table 3. Test accuracy of weldment 1
    MethodAccuracy /%
    Secondweldment testMixeddata test
    GS93.390.1
    SURF94.489.6
    HC87.586.7
    LSE94.388.2
    GH91.290.1
    GXN96.796.3
    Table 4. Test results of multiple weldments
    MethodTime /ms
    GS35
    SURF27
    HC17
    LSE39
    GH37
    GXN36
    Table 5. Time consumption of each method
    Meiju Liu, Bo Yun. Application of Deep Convolution Network Compression Algorithm in Weld Recognition[J]. Laser & Optoelectronics Progress, 2019, 56(5): 051004
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