- Identified road surfaces and 13 different classes like speed bumps, paved, unpaved, markings, water puddles, potholes, etc.
- Trained the model for semantic segmentation on Unet architecture along with backbone architectures like Resnet, InceptionNet and VGGnet.
- Added mask to images to show the classes according to their respective colors.
| Sl. No. | Model | Epochs | Mean IoU Score on CV |
|---|---|---|---|
| 1. | UNet | 20 | 0.26527 |
| 2. | UNet with ResNet18 | 10 | 0.6309 |
| 3. | UNet with ResNet34 | 100 | 0.7297 |
| 4. | UNet with InceptionNetV3 | 20 | 0.6633 |
| 5. | UNet with VGGnet16 | 20 | 0.6604 |
| Sl. No. | Color | Category |
|---|---|---|
| 1. | Black | Background |
| 2. | Light Blue | Road Asphalt |
| 3. | Greenish Blue | Paved Road |
| 4. | Peach/Light Orange | Unpaved Road |
| 5. | White | Road Marking |
| 6. | Pink | Speed Bump |
| 7. | Yellow | Cats Eye |
| 8. | Purple | Storm Drain |
| 9. | Cyan | Manhole Cover |
| 10. | Dark Blue | Patches |
| 11. | Dark Red | Water Puddle |
| 12. | Red | Pothole |
| 13. | Orange | Cracks |