Algorithm Implementation for Line Detection Using Hough Transform

In this article, we discuss the algorithmic implementation of line detection using Hough Transform. This method represents a fundamental computer vision technique for detecting straight lines in digital images. The key implementation aspect involves configuring different threshold parameters to control line detection output. By adjusting the accumulator threshold (typically implemented via the HoughLines or HoughLinesP functions), developers can regulate the quantity and minimum length of detected lines. The algorithm implementation typically follows these steps: first, apply edge detection using methods like Canny edge detector (cv2.Canny() in OpenCV), then transform edge points to Hough parameter space, and finally apply voting threshold to identify significant lines. Additional optimization techniques include using probabilistic Hough transform (HoughLinesP) for better performance with line segments. Overall, the Hough transform-based line detection algorithm serves as a critical technology powering various applications including autonomous driving systems and robotic navigation, where reliable line detection is essential for path planning and environmental perception.