Research Article
Improvement of Multiclass Classification of Pavement Objects Using Intensity and Range Images
Table 2
Comparison of deep CNNs for classification of pavement objects using single-scale intensity and range input tiles.
| Metric | Method | Input image | Crack | Crack seal | Patch | Pothole | Marker | Manhole | Curbing | Asphalt | Avg |
| Precision | VGG16 | Intensity | 0.660 | 0.533 | 0.676 | 0.529 | 0.923 | 0.850 | 0.947 | 0.945 | 0.758 | Intensity + Range | 0.732 | 0.684 | 0.849 | 0.637 | 0.944 | 0.875 | 0.956 | 0.959 | 0.830 | VGG19 | Intensity | 0.593 | 0.577 | 0.653 | 0.636 | 0.932 | 0.840 | 0.944 | 0.95 | 0.766 | Intensity + Range | 0.670 | 0.707 | 0.823 | 0.570 | 0.940 | 0.887 | 0.955 | 0.963 | 0.814 | ResNet50 | Intensity | 0.637 | 0.702 | 0.661 | 0.554 | 0.928 | 0.831 | 0.945 | 0.949 | 0.776 | Intensity + Range | 0.679 | 0.777 | 0.793 | 0.593 | 0.931 | 0.879 | 0.954 | 0.959 | 0.821 | DenseNet121 | Intensity | 0.647 | 0.529 | 0.688 | 0.543 | 0.928 | 0.850 | 0.946 | 0.949 | 0.760 | Intensity + Range | 0.737 | 0.875 | 0.789 | 0.608 | 0.935 | 0.895 | 0.952 | 0.961 | 0.844 | DACNN (ours) | Intensity | 0.864 | 0.897 | 0.965 | 0.947 | 0.966 | 0.919 | 0.983 | 0.986 | 0.941 | Intensity + Range | 0.887 | 0.942 | 0.972 | 0.953 | 0.971 | 0.965 | 0.993 | 0.987 | 0.959 |
| Recall | VGG16 | Intensity | 0.256 | 0.018 | 0.345 | 0.253 | 0.917 | 0.646 | 0.985 | 0.988 | 0.551 | Intensity + Range | 0.438 | 0.241 | 0.563 | 0.463 | 0.908 | 0.669 | 0.988 | 0.985 | 0.657 | VGG19 | Intensity | 0.373 | 0.033 | 0.348 | 0.139 | 0.904 | 0.669 | 0.991 | 0.981 | 0.555 | Intensity + Range | 0.480 | 0.209 | 0.596 | 0.571 | 0.920 | 0.727 | 0.986 | 0.983 | 0.684 | ResNet50 | Intensity | 0.33 | 0.073 | 0.364 | 0.271 | 0.911 | 0.618 | 0.990 | 0.984 | 0.568 | Intensity + Range | 0.453 | 0.163 | 0.533 | 0.450 | 0.925 | 0.645 | 0.988 | 0.984 | 0.643 | DenseNet121 | Intensity | 0.324 | 0.100 | 0.363 | 0.319 | 0.912 | 0.655 | 0.990 | 0.984 | 0.581 | Intensity + Range | 0.431 | 0.218 | 0.648 | 0.528 | 0.926 | 0.676 | 0.992 | 0.985 | 0.676 | DACNN (ours) | Intensity | 0.780 | 0.837 | 0.942 | 0.909 | 0.957 | 0.937 | 0.990 | 0.991 | 0.918 | Intensity + Range | 0.805 | 0.947 | 0.958 | 0.956 | 0.966 | 0.937 | 0.990 | 0.993 | 0.944 |
| F-score | VGG16 | Intensity | 0.369 | 0.034 | 0.457 | 0.343 | 0.920 | 0.734 | 0.966 | 0.966 | 0.599 | Intensity + Range | 0.548 | 0.356 | 0.677 | 0.536 | 0.926 | 0.758 | 0.972 | 0.972 | 0.718 | VGG19 | Intensity | 0.458 | 0.063 | 0.454 | 0.223 | 0.918 | 0.745 | 0.967 | 0.965 | 0.599 | Intensity + Range | 0.560 | 0.323 | 0.691 | 0.602 | 0.930 | 0.799 | 0.970 | 0.973 | 0.731 | ResNet50 | Intensity | 0.434 | 0.133 | 0.469 | 0.364 | 0.919 | 0.709 | 0.967 | 0.966 | 0.620 | Intensity + Range | 0.543 | 0.269 | 0.638 | 0.512 | 0.928 | 0.744 | 0.971 | 0.971 | 0.697 | DenseNet121 | Intensity | 0.432 | 0.169 | 0.475 | 0.402 | 0.920 | 0.740 | 0.967 | 0.966 | 0.634 | Intensity + Range | 0.544 | 0.349 | 0.712 | 0.566 | 0.930 | 0.770 | 0.971 | 0.973 | 0.727 | DACNN | Intensity | 0.820 | 0.866 | 0.953 | 0.928 | 0.961 | 0.928 | 0.986 | 0.988 | 0.929 | Intensity + Range | 0.844 | 0.944 | 0.965 | 0.954 | 0.969 | 0.951 | 0.992 | 0.990 | 0.951 |
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