Road Centerline Detection with MATLAB Implementation

Road centerline detection serves as a critical technology in autonomous driving and intelligent transportation systems, primarily determining the correct vehicle navigation path. MATLAB, with its powerful Image Processing Toolbox, provides an ideal environment for implementing this functionality. The complete detection pipeline typically involves the following core steps:

The process begins with preprocessing captured road images, including grayscale conversion to reduce computational complexity and Gaussian filtering to eliminate image noise interference. The quality of preprocessed images directly impacts subsequent detection accuracy. In MATLAB implementation, this can be achieved using rgb2gray() for color conversion and imgaussfilt() for noise reduction with customizable sigma parameters.

During the edge detection phase, an improved Canny operator is commonly employed to effectively extract road boundary features. Compared to traditional operators, the enhanced version adapts better to various lighting conditions through dual-threshold adjustment. The edge() function in MATLAB with 'Canny' method allows specification of threshold values [low_thresh, high_thresh] for optimal performance across different road scenarios.

Hough transform constitutes the core algorithm for centerline detection, particularly suitable for road images with distinct linear characteristics. This algorithm converts pixel points in the image into parameter space curves through parametric space transformation, where the parameter combination with the most intersections represents the optimal road line. For curved roads, piecewise linear fitting approaches can be applied. MATLAB's hough() and houghpeaks() functions facilitate this transformation and peak detection, while houghlines() extracts line segments based on theta-rho parameters.

For more complex road scenarios, machine vision assistance techniques can be introduced. By training classifiers to recognize lane marking features and combining them with traditional image processing methods, detection robustness can be significantly improved. This approach performs exceptionally well under challenging conditions such as illumination variations and shadow occlusions. MATLAB's Computer Vision Toolbox offers functions like trainCascadeObjectDetector() for creating custom detectors and vision.BlobAnalysis for feature extraction.

The experimental validation phase requires preparing diverse road scene samples covering different weather conditions, illumination intensities, and road types. By comparing deviations between detection results and actual centerlines, algorithm performance can be objectively evaluated. Typical evaluation metrics include detection accuracy, false positive rate, and real-time performance. MATLAB provides evaluation tools like confusionmat() for accuracy calculation and tic-toc functions for timing analysis.