This project focuses on drivable area detection using the BDK100 dataset and a UNet model. The goal is to accurately identify lane lines which you can drive in, which is a critical task for autonomous driving systems.
Click here for dataset description and download
I have implemented a UNet model for drivable area segmentation. The UNet architecture consists of an encoder-decoder structure that helps in capturing both local and global information for precise segmentation.
- Encoder: Custom CNN model was used here as in the paper
- Decoder: Upsampling with skip connections
- Loss function: Dice loss
- Optimizer: Adam
to run this project locally, download the utils.py file, which has the required utils to run the UNet_code