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Identification of road surfaces and 12 different classes like speed bumps, paved, unpaved, markings, water puddles, potholes, etc.

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Shrinidhi1/Multiclass-Semantic-Segmentation-for-Road-Surface-Detection

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Multiclass Semantic Segmentation for Road Surface Detection

  1. Identified road surfaces and 13 different classes like speed bumps, paved, unpaved, markings, water puddles, potholes, etc.
  2. Trained the model for semantic segmentation on Unet architecture along with backbone architectures like Resnet, InceptionNet and VGGnet.
  3. Added mask to images to show the classes according to their respective colors.

Model Training

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

Markings

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

Deployed On Hugging Face: Link

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Identification of road surfaces and 12 different classes like speed bumps, paved, unpaved, markings, water puddles, potholes, etc.

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