Vision-Guided Vehicle Road Recognition

Vision-guided road recognition technology serves as the core component for precise navigation in Automated Guided Vehicles (AGVs). Its primary task involves real-time interpretation of ground markers to ensure the vehicle follows a predefined trajectory. Below are implementation logics for several mainstream technical approaches: Grayscale Image Processing Solution For monochrome guiding line scenarios, the typical workflow begins with Gaussian filtering to reduce image noise, followed by adaptive threshold segmentation using Otsu's method to highlight path regions. Morphological closing operations eliminate minor noise, and connected component analysis extracts path contours. This method offers low computational complexity but relies on high-contrast environments. Implementation involves using OpenCV functions like cv2.GaussianBlur() for noise reduction, cv2.threshold() with OTSU flag for binarization, and morphological operations with cv2.morphologyEx(). Color Space Recognition Solution In complex lighting or multi-color path scenarios, traditional RGB space is susceptible to brightness interference. Converting to HSI color model leverages the stability of the Hue channel to isolate specific colored paths. For example, red paths exhibit characteristic values of 0° or 360° in the H channel, enabling target area extraction through threshold segmentation. Code implementation typically uses cv2.cvtColor() for color space conversion and channel separation, followed by histogram analysis and inRange() thresholding. Path Centerline Localization After obtaining binarized path regions, the scanning line method vertically projects path edge points, with least squares fitting used to derive centerline equations. Advanced implementations incorporate sliding window mechanisms to dynamically track path direction, adapting to curved trajectories. Algorithm implementation involves creating a pixel density histogram along the vertical axis and using numpy.polyfit() for polynomial curve fitting. Radon Transform Deviation Detection When quantifying path deviation angles, Radon transform is applied to segmented images. This algorithm detects line orientation through projection integration, calculating the angle between the path and vehicle coordinate system to provide correction parameters for control systems. Its advantage lies in robustness against partial occlusions or broken lines. Implementation utilizes scipy.transform.radon() for projection calculations and peak detection algorithms to identify dominant line orientations. These methods can be combined based on actual scenario complexity. For instance, warehouse logistics applications prioritize HSI+Radon solutions to handle variable lighting conditions, while structured environments like industrial assembly lines can satisfy real-time requirements with grayscale processing alone. The selection criteria typically balance between computational efficiency (using OpenCV-optimized functions) and environmental adaptability (employing multi-algorithm fusion).