Table 2 Comparison on the CamVid dataset [16] using 11 road scene categories (in percent)
From: Deep residual coalesced convolutional network for efficient semantic road segmentation
Method | Sky | Building | Road | Sidewalk | Car | Pedestrian | Bicyclist | Tree | Fence | Column-pole | Sign-symbol | Class avg. | Class IoU |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Local label descriptor [1] | 88.8 | 80.7 | 98 | 12.4 | 16.4 | 1.09 | 0.07 | 61.5 | 0.05 | 4.13 | n/a | 36.3 | n/a |
Boosting+pairwise CRF [2] | 94.7 | 70.7 | 94.1 | 79.3 | 74.4 | 45.7 | 23.1 | 70.8 | 37.2 | 13 | 55.9 | 59.9 | n/a |
Boosting+detection+CRF [3] | 96.2 | 81.5 | 93.9 | 81.5 | 78.7 | 43 | 33.9 | 76.6 | 47.6 | 14.3 | 40.2 | 62.5 | n/a |
Dense depth map [4] | 95.4 | 85.3 | 98.5 | 38.1 | 69.2 | 23.8 | 28.7 | 57.3 | 44.3 | 22 | 46.5 | 55.4 | n/a |
Super parsing [5] | 96.9 | 87 | 95.9 | 70 | 62.7 | 14.7 | 19.4 | 67.1 | 17.9 | 1.7 | 30.1 | 51.2 | n/a |
SegNet-basic [8] | 91.2 | 75 | 93.3 | 74.1 | 82.7 | 55 | 16 | 84.6 | 47.5 | 44.8 | 36.9 | 62 | 47.7 |
SegNet [8] | 92.4 | 88.8 | 97.2 | 84.4 | 82.1 | 57.1 | 30.7 | 87.3 | 49.3 | 27.5 | 20.5 | 65.2 | 55.6 |
ENet [9] | 95.1 | 74.7 | 95.1 | 86.7 | 82.4 | 67.2 | 34.1 | 77.8 | 51.7 | 35.4 | 51 | 68.3 | 51.3 |
RCC-Net (sum) | 95.2 | 70.1 | 94.1 | 90.1 | 82.6 | 70.6 | 45.7 | 81.2 | 51 | 52.3 | 35.4 | 69.8 | 52.6 |
RCC-Net (concatenated) | 94.3 | 71.8 | 92.6 | 92.7 | 79.3 | 57.7 | 65.6 | 80.5 | 35.7 | 57.4 | 59.4 | 71.5 | 53.3 |