A Clustering-Based Algorithm for Automatic Detection of Automobile Dashboard

This paper presents an automatic detection system capable of detecting an automobile dashboard with high accuracy. Since the structure of an automobile dashboard is quite different from general instruments, commonly used algorithms for instrument detection can hardly meet the accuracy and robustness. In this paper, a novel approach is presented to detect an automobile dashboard. The contour retrieving algorithm is first performed to extract contour image of dashboard. Progressive Probabilistic Hough Transform (PPHT) is then applied to fit accurate pointer position. A K-means clustering based method is then proposed recognizing tick marks. Since the result of K-means clustering always includes noise items, Z-test is introduced to insure precise clustering result. Lagrange interpolation method is used to describe the precise linear relationship between angle and reading of the instrument. The experimental test shows robustness and precision of this detection algorithm.