amario1306619051/advanced-lane-detection

Advanced Lane Detection with Sliding Windows Dive into computer vision with advanced lane detection using the sliding windows technique. This repository demonstrates how to identify and track lanes on road images or videos. Explore the intricacies of image processing and lane tracking algorithms to enhance your understanding of self-driving car te

Advanced Lane Detection using Sliding Windows

![Lane Detection]("lane detection.jpeg")

This repository contains a Python implementation of an advanced lane detection system using the sliding windows technique. The goal of this project is to accurately identify and track lanes on the road, contributing to the development of self-driving car technology and advanced driver assistance systems.

Features

• Detects lane lines using computer vision techniques.
• Utilizes sliding windows for accurate lane tracking.
• Robust to varying lighting and road conditions.
• Provides visualization of the lane detection process.

Prerequisites

• Python (>=3.6)
• OpenCV (pip install opencv-python)
• NumPy (pip install numpy)
• Matplotlib (pip install matplotlib)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/advanced-lane-detection.git
   cd advanced-lane-detection

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Place your video file or images in the input/ directory.

2. Run the lane detection script:

   python lane_detection.py

3. The processed output will be saved in the output/ directory with lane markings and detection information.

How it Works

The advanced lane detection system primarily uses the following steps:

1. Camera Calibration: Corrects for the distortion in the images caused by the camera lens.

2. Perspective Transformation: Applies a perspective transform to obtain a top-down view of the road, making lane detection easier.

3. Color and Gradient Thresholding: Utilizes color spaces and gradient thresholds to highlight lane lines.

4. Sliding Windows: Implements sliding windows to identify and track lane pixels in the image.

5. Polynomial Fitting: Fits a polynomial to the detected lane pixels.

6. Inverse Perspective Transformation: Maps the detected lane region back onto the original perspective.

7. Visualization: Draws the detected lanes onto the original image, providing a visual representation of the lane detection process.

Results

The lane detection system is capable of accurately identifying and tracking lanes even under challenging conditions such as varying lighting and road textures.

Contributing

Contributions to this project are welcome! If you find any issues or have ideas for improvements, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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