Method Details
Details for method 'EaNet-V1'
Method overview
| name | EaNet-V1 |
| challenge | pixel-level semantic labeling |
| details | Parsing very high resolution (VHR) urban scene images into regions with semantic meaning, e.g. buildings and cars, is a fundamental task necessary for interpreting and understanding urban scenes. However, due to the huge quantity of details contained in an image and the large variations of objects in scale and appearance, the existing semantic segmentation methods often break one object into pieces, or confuse adjacent objects and thus fail to depict these objects consistently. To address this issue, we propose a concise and effective edge-aware neural network (EaNet) for urban scene semantic segmentation. The proposed EaNet model is deployed as a standard balanced encoder-decoder framework. Specifically, we devised two plug-and-play modules that append on top of the encoder and decoder respectively, i.e., the large kernel pyramid pooling (LKPP) and the edge-aware loss (EA loss) function, to extend the model ability in learning discriminating features. The LKPP module captures rich multi-scale context with strong continuous feature relations to promote coherent labeling of multi-scale urban objects. The EA loss module learns edge information directly from semantic segmentation prediction, which avoids costly post-processing or extra edge detection. During training, EA loss imposes a strong geometric awareness to guide object structure learning at both the pixel- and image-level, and thus effectively separates confusing objects with sharp contours. |
| publication | Parsing Very High Resolution Urban Scene Images by Learning Deep ConvNets with Edge-Aware Loss Xianwei Zheng, Linxi Huan, Gui-Song Xia, Jianya Gong |
| project page / code | |
| used Cityscapes data | fine annotations |
| used external data | ImageNet |
| runtime | n/a |
| subsampling | no |
| submission date | May, 2020 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| IoU Classes | 81.6836 |
| iIoU Classes | 59.6017 |
| IoU Categories | 91.1883 |
| iIoU Categories | 77.8342 |
Class results
| Class | IoU | iIoU |
|---|---|---|
| road | 98.7604 | - |
| sidewalk | 87.1909 | - |
| building | 93.7706 | - |
| wall | 67.2685 | - |
| fence | 64.1345 | - |
| pole | 67.467 | - |
| traffic light | 75.6267 | - |
| traffic sign | 79.8804 | - |
| vegetation | 93.7732 | - |
| terrain | 72.4115 | - |
| sky | 95.6646 | - |
| person | 86.9444 | 65.8753 |
| rider | 73.4417 | 49.4382 |
| car | 96.045 | 90.746 |
| truck | 80.3559 | 44.8858 |
| bus | 89.425 | 54.8061 |
| train | 80.5463 | 58.9446 |
| motorcycle | 71.7323 | 48.827 |
| bicycle | 77.5501 | 63.2907 |
Category results
| Category | IoU | iIoU |
|---|---|---|
| flat | 98.7694 | - |
| nature | 93.5023 | - |
| object | 73.8175 | - |
| sky | 95.6646 | - |
| construction | 93.8809 | - |
| human | 86.846 | 66.7768 |
| vehicle | 95.8373 | 88.8916 |