Road Segmentation
The goal of this project is to create an AI able to segment in real time some categories of objects on the road. All images are segmented independently.
Models
I used those models to segment the video :
-
BiSeNet V2 - paper - pretrained model
Result : Mean Intersection Over Union = 54% Loss = 0.23 -
Attention R2U-Net - pretrained model
Result : Mean Intersection Over Union = 55% Loss = 0.21 -
DDRNet - paper
-
TMANet - paper
I trained each of these models for about 48 hours with an I7-7700K, a 6GB GTX 1060 and 28GB of RAM.
Usage
Testing Segmentation
To test the models on a video, you can use the UI.
First, install required packages :
pip install -r requirements.txt
Then, start the UI :
python segmentation.py [Video Folder Path]
Training
To train a model, you first need to download the A2D2 and Mappillary Vistas dataset.
Then, install required packages :
pip install -r requirements.txt
After that, you might need to change some constant (dataset folders, epochs, lr, WanDB, ...) in the file train.py :
code train.py
Finally, start the learning :
python train.py
Categories
Those are the categories trained to be segmented by the AI.
| # | Name | Color |
|---|---|---|
| 1 | Road | |
| 2 | Lane | |
| 3 | Crosswalk | |
| 4 | Curb | |
| 5 | Sidewalk | |
| 6 | Traffic Light | |
| 7 | Traffic Sign | |
| 8 | Person | |
| 9 | Bicycle | |
| 10 | Bus | |
| 11 | Car | |
| 12 | Motorcycle | |
| 13 | Truck | |
| 14 | Sky | |
| 15 | Nature |
Datasets
The AI was trained using a mix of those two datasets :
Tools
List of tools I used :