Semantic Segmentation On Pothole Mix
Metrics
Test Dice Multiclass
Test mIoU
Validation Dice Multiclass
Validation mIoU
Results
Performance results of various models on this benchmark
Model Name | Test Dice Multiclass | Test mIoU | Validation Dice Multiclass | Validation mIoU | Paper Title | Repository |
---|---|---|---|---|---|---|
HCMUS-SegFormer | 0 .747 | 0 .628 | 0 .637 | 0 .523 | SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data | |
Baseline - DeepLabv3+ | 0 .789 | 0 .676 | 0 .814 | 0 .711 | SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data | |
HCMUS-CPS-DLU-Net | 0 .789 | 0 .677 | 0 .763 | 0 .647 | SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data | |
PUCP-Unet++ | 0 .832 | 0 .731 | 0 .800 | 0 .694 | SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data | |
PUCP-Unet | 0 .824 | 0 .720 | 0 .804 | 0 .698 | SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data | |
HCMUS-DeepLabv3+ | 0 .823 | 0 .719 | 0 .802 | 0 .695 | SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data | |
PUCP-MAnet | 0 .827 | 0 .725 | 0 .810 | 0 .705 | SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data |
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