This repository contains machine learning models and algorithms for autonomous vehicle perception systems developed by Team 02.

This repository serves as a collection of machine learning solutions for various perception tasks in autonomous driving:

• Lane Detection: Computer vision and ML algorithms for identifying lane markings
• Object Detection: Detection and classification of various objects (vehicles, pedestrians, traffic signs, etc.)
• Traffic Sign Recognition: Image Classifier model to make the classification of traffic signs and traffic lights (red, green, yellow, 50 km/h, 80 km/h, yield, stop, crosswalk, danger)

MachineLearning/
├── LaneDetection/     # Lane detection models and algorithms
├── ObjectDetection/   # Object detection and classification models
├── TrafficSignRecognition/   # Traffic Signs and Traffic Lights classification models
└── README.md         # This documentation file

The Lane Detection module contains models and tools for identifying lane markings, determining lane boundaries.

The Object Detection module provides models for detecting, classifying, and tracking various objects relevant to autonomous driving scenarios.

The Traffic Sign Recognition module provides models for classifying signs and traffic lights,relevant to autonomous driving scenarios.

Each subdirectory contains its own documentation with specific setup instructions.