Vision-Guided Vehicle Road Recognition

This paper discusses several methods in vision-guided vehicle road recognition. These methods include:

- Grayscale AGV Path Recognition: Typically implemented through image preprocessing techniques like Gaussian filtering and edge detection algorithms (e.g., Canny operator) to extract path features from grayscale images

- Color-based AGV Path Recognition: Utilizes color thresholding in RGB space with functions like inRange() to segment path regions based on specific color signatures

- HSI Color Space AGV Path Recognition: Employs HSI color model conversion to achieve illumination-invariant path detection, where hue and saturation components are used for robust color segmentation

- Path Centerline Localization: Implements algorithms like skeletonization or Hough transform to extract the central trajectory, often using morphological operations and curve fitting techniques

- AGV Path Deviation Detection using Radon Transform: Applies Radon transform for line detection and angle calculation to quantify navigation errors through projection-based analysis

By implementing these methods, vehicle path recognition performance can be significantly improved, enabling more efficient navigation and control systems. Among these techniques, path centerline localization and Radon transform-based deviation detection represent relatively advanced approaches that play crucial roles in practical applications. These methods show promising potential to become key research directions in future autonomous navigation studies.