This project implements a lane line detection system using computer vision techniques and AI algorithms. The system processes video input to identify and highlight lane lines, essential for various applications in autonomous driving, lane departure warning systems, and advanced driver assistance systems (ADAS).
The lane line detection pipeline comprises several essential steps:
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Gaussian Smoothing: Applies Gaussian blur to the input image to reduce noise and detail, facilitating more accurate edge detection in subsequent steps.
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Grayscale Conversion: Converts the input image into a grayscale representation, simplifying further processing and reducing computational complexity.
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Canny Edge Detection: Utilizes the Canny edge detection algorithm to identify significant changes in pixel intensity, effectively detecting edges, including lane lines, in the image.
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Region Masking: Masks out irrelevant parts of the image, focusing the lane detection process on the region of interest (typically the lower half of the image), where lane lines are expected to appear.
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Hough Transformation: Detects lines in the masked image space using the Hough transform, converting them from Cartesian to Hough parameter space. This step effectively identifies straight lines, potentially representing lane lines.
To use this project:
- Clone the repository to your local machine.
- Install the necessary dependencies, preferably using a virtual environment.
- Run the main script to process a video file or live camera feed.
- View the output video with lane lines highlighted.
This project relies on the following libraries:
- OpenCV: For image processing and computer vision tasks.
- NumPy: For numerical operations and array manipulation.
Ensure these dependencies are installed before running the project.
Possible enhancements and extensions to this project include:
- Implementing a more robust algorithm to handle challenging road conditions like shadows, varying lighting conditions, and curved lane lines.
- Integrate machine learning techniques for lane detection to improve accuracy and adaptability to diverse environments.
- Develop a graphical user interface (GUI) for easier interaction and visualization of results.
This project is licensed under the MIT License. Feel free to use, modify, and distribute it according to the terms of the license.
For more details, refer to the LICENSE file.
Special thanks to the creators and contributors of OpenCV and NumPy for their invaluable contributions to the field of computer vision and image processing.