(c) (d)
Figure 19. Error Results of Hough-kmeans and Improved Hough-kmeans Algorithms.
Comparing the method proposed in this article with the probabilistic Hough line detection method and the LSD line detection method, it can be seen in Figure 21a that the probability Hough line detection has a high false detection and missed detection rate, and some unrelated background lines have been detected, with a large number of lines. The LSD line detection method in Figure 21b also suffers from false detections, with the detected lines being intermittent and a large number of interfering lines in the background being detected. As shown in Figure 21c, the original k-means line fitting algorithm has the advantages of low false detection and missed detection rates. However, when the Hough transform threshold is low, the detection error increases or even fails.The 21d line fitting algorithm proposed in this article only displays the four lines we need after clustering, and still has good detection performance and high robustness even in the presence of significant interference in the edge image.