Advances in Civil Engineering / 2020 / Article / Tab 4 / Research Article
A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine Table 4 Prediction result comparison.
Phase Indices L-SHADE-SVM-SVD DCNN-Rmsprop DCNN-Adam DCNN-Sgdm MB-BPNN Mean Std. Mean Std. Mean Std. Mean Std. Mean Std. Training CAR (%) 97.417 0.222 87.922 2.585 89.211 1.937 87.756 6.165 86.763 6.889 TP 437.200 4.938 369.000 25.984 390.250 17.693 375.650 40.359 333.900 63.570 TN 439.550 5.753 422.300 20.683 412.650 21.755 414.150 21.866 360.200 9.540 FP 9.550 1.605 27.700 20.683 37.350 21.755 35.850 21.866 66.100 63.570 FN 13.700 1.302 81.000 25.984 59.750 17.693 74.350 40.359 39.800 9.540 Precision 0.979 0.004 0.934 0.044 0.916 0.042 0.912 0.064 0.835 0.159 Recall 0.970 0.003 0.820 0.058 0.867 0.039 0.835 0.090 0.895 0.012 NPV 0.970 0.003 0.842 0.041 0.875 0.029 0.851 0.065 0.901 0.024 F1 score 0.974 0.002 0.871 0.030 0.889 0.019 0.870 0.075 0.852 0.132 Testing CAR (%) 92.600 2.761 88.350 3.133 86.900 4.204 86.800 6.178 85.700 7.248 TP 46.200 4.873 41.700 3.246 42.200 3.792 40.900 4.767 40.900 8.130 TN 46.400 5.305 46.650 2.323 44.700 3.326 45.900 2.674 44.800 2.587 FP 4.500 1.878 3.350 2.323 5.300 3.326 4.100 2.674 9.100 8.130 FN 2.900 2.125 8.300 3.246 7.800 3.792 9.100 4.767 5.200 2.587 Precision 0.911 0.037 0.929 0.045 0.893 0.057 0.909 0.065 0.818 0.163 Recall 0.942 0.040 0.834 0.065 0.844 0.076 0.818 0.095 0.890 0.042 NPV 0.940 0.044 0.852 0.049 0.856 0.059 0.839 0.067 0.896 0.052 F1 score 0.926 0.027 0.877 0.036 0.865 0.044 0.859 0.076 0.839 0.137