Skip to content

Latest commit

 

History

History
47 lines (34 loc) · 1.46 KB

README.md

File metadata and controls

47 lines (34 loc) · 1.46 KB

Installation

$pip install -r requirments.txt

Semantic Segmentation

Implement Semantic Segmentation in tensorflow, successfully trained segnet-basic in Indian driving conditions dataset by Intel and IIIT hyderabad.

The model segments any input image into 14 classes along with the labels as follows

  • Background(0)
  • Fence and walls(1)
  • Road(2)
  • Billboards, poles and bridges(3)
  • Riders and regular vehicles (4)
  • People and animals(5)
  • Sidewalk(6)
  • Curb(7)
  • Parking(8)
  • Drivable fallback(damaged parts of the road)(9)
  • Heavy duty vehicles(truck, trailers, etc.)(10)
  • Non-drivable fallback(damaged sidewalks, etc.)(11)
  • Obstacles(garbage dumps, boulders)(12)
  • Unlabelled(13)

Road Damage detector

Implemented Single Shot detector with MobileNet in tensorflow. The model detects the following 10 classes in input image and creates Bounding Box

  • alt text

Requirement

Check requirements.txt

Instructions for Running the code

Put all the testing images in a folder named test (the image file name should not contain white space).

Command for testing: $python run.py --folder test/ The Sematically Segmented images will be saved in the folder named out_image(each pixel lies between the value 0 to 13 depending on the class it belongs) and images with road damage detection will be saved in the folder named road_damage. The quality factor will be printed on the terminal along with the name of input image.