MATLAB Implementation of Lane Line Detection with Code Description

Basic Approach for Lane Line Detection in MATLAB

Lane line detection is a fundamental task in autonomous driving and computer vision. Implementing this functionality in MATLAB typically requires combining image processing and geometric transformation techniques. The following outlines the standard implementation workflow:

Image Preprocessing First, convert the input road image to grayscale and apply noise reduction. The commonly used method is Gaussian filtering, which smooths the image and reduces noise interference, providing clearer input for subsequent edge detection. In MATLAB, this can be implemented using rgb2gray() for color conversion and imgaussfilt() for Gaussian filtering.

Edge Detection Use Canny or Sobel operators to detect edges in the image. The Canny operator is particularly common in lane line detection due to its excellent noise resistance and edge connectivity capabilities. This step generates a binary image where white pixels represent edges. MATLAB's edge() function with the 'Canny' option provides an efficient implementation.

Region of Interest (ROI) Extraction Use a mask to filter out irrelevant areas, retaining only regions where lane lines are likely to appear (such as trapezoidal areas). This step reduces computational load and eliminates interfering edges. The roipoly() function in MATLAB can be used to define and apply polygonal masks.

Hough Transform for Line Detection Apply Hough transform on the edge image to detect potential line segments. Hough transform maps lines from image space to parameter space (polar coordinates), identifying the most prominent lines through a voting mechanism. MATLAB's hough() and houghlines() functions implement this transform with customizable parameters for line detection sensitivity.

Lane Line Fitting and Filtering Analyze the slope and intercept of detected lines, filtering out segments that match lane line geometric characteristics (left lane lines typically have negative slopes, right lane lines positive slopes). Finally, use least squares method to fit complete lane lines. The polyfit() function in MATLAB can efficiently perform polynomial fitting for this purpose.

Result Visualization Overlay the detected lane lines onto the original image to verify algorithm accuracy. Typically, use different colors to mark left and right lane lines, extending line segments to cover the entire field of view. MATLAB's insertShape() function is useful for drawing lines on images with custom colors and transparency.

Experimental Results and Optimization Directions In standard road images, this method can reliably detect lane lines, but may require further optimization for complex scenarios (such as shadows, road wear). Consider incorporating color space segmentation (like white/yellow channels in HSV) or machine learning methods (such as LSD algorithm) to improve robustness. The vision.CascadeObjectDetector() or custom machine learning models can be integrated for enhanced performance.

Through the above steps, MATLAB can efficiently implement lane line detection, making it suitable as an introductory experiment in computer vision education and research.