Lane_detection_project

Detection of Road Lane Lines - Computer Vision project using OpenCV & Python

🎯 Project Overview

A comprehensive lane detection system implementing both traditional computer vision and deep learning approaches for Data Science Pinnacle internship evaluation.

✨ Features

• Dual Approach: Traditional CV + Deep Learning comparison
• Real-time Processing: 30+ FPS on standard hardware
• Multiple Algorithms: Hough Transform, Sliding Window, U-Net
• Comprehensive Metrics: Accuracy, IoU, F1-Score, Processing Time
• Robust Pipeline: Handles various road conditions

📊 Results

• Accuracy: 92% on TuSimple datasets
• FPS: 25+ on standard laptop
• Demo: Watch here

Tech Stack

OpenCV | NumPy | Streamlit | Python 3.9+

Usage:

1. Set up the environment

conda env create -f environment.yml

To activate the environment:

Window: conda activate carnd

Linux, MacOS: source activate carnd

2. Run the pipeline:

python main.py INPUT_IMAGE OUTPUT_IMAGE_PATH
python main.py --video INPUT_VIDEO OUTPUT_VIDEO_PATH

Installation

# Clone repository
git clone https://github.com/techindro/Lane_detection_project.git
cd lane-detection-dsp

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
streamlit run app.py.