Mathematical Problems in Engineering / 2018 / Article / Tab 1 / Research Article
Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance Table 1 Prediction result comparison.
Statistical āMeasure CAR (%) Pavement Crack Classification Models NBC CT BPANN RBFNN SVM LSSVM Average AC 65.89 81.72 80.33 70.17 93.78 95.33 DC 66.94 68.28 84.44 70.28 92.17 94.00 LC 78.78 73.89 82.94 74.94 88.06 87.67 NC 87.94 79.67 85.44 87.94 91.00 91.17 TC 78.17 80.67 90.78 70.72 94.56 94.94 Overall 75.54 76.84 84.79 74.81 91.91 92.62 Standard Deviation (Std.) AC 7.18 4.09 5.94 8.07 4.17 3.46 DC 3.48 6.38 5.88 5.03 3.31 3.02 LC 4.75 6.21 4.08 5.28 4.78 4.48 NC 4.24 5.24 5.57 4.37 3.65 3.56 TC 4.53 4.96 3.27 6.60 3.36 2.08 Overall 1.90 2.19 2.32 2.44 1.53 1.46
Note: AC: alligator crack; DC: diagonal crack; LC: longitudinal crack; NC: noncrack; TC: transverse crack.