Semantic Segmentation of Roads in Cityscapes
Implemented four different approaches for semantic segmentation of road classes using the Cityscapes dataset.
Overview
This project focused on implementing and comparing different approaches for semantic segmentation of road classes in urban scenes using the Cityscapes dataset. The goal was to develop robust methods for identifying and classifying different types of roads and their boundaries in complex urban environments.
Approaches
Single Pixel Classifier
Implemented a baseline approach using traditional computer vision techniques and pixel-level classification.
Patch Classification
Developed a patch-based approach that analyzes local image regions for road classification.
FCNNs
Implemented Fully Convolutional Neural Networks for end-to-end semantic segmentation.
U-NETs
Designed and trained U-NET architectures for precise road boundary detection.
Results
- Achieved 85% accuracy in road class segmentation
- Successfully implemented all four approaches with detailed comparisons
- Developed efficient data preprocessing pipeline
- Created comprehensive evaluation metrics
Technical Details
- Used PyTorch for deep learning implementations
- Implemented custom data loaders for Cityscapes dataset
- Developed visualization tools for segmentation results
- Created modular architecture for easy comparison of approaches
Project Documentation
Want to learn more about the technical details of this project? Download our detailed presentation PDF.