Method of Rail Surface Extraction Based on Greedy Selection and Slope Detection Expansion

Abstract

The traditional rail surface area extraction method has some preconditions, for instance, the width of rail surface should be given in advance, the rail surface should be assumed to be in the center of the rail image and the boundary should be selected manually. Besides, it also has some problems, such as poor self-adaptability, light sensitivity, and the inability to extract the rail surface completely when there are noises such as dust and mud at the rounded corners of the rail head. Aiming at the above problems, a method of extracting rail surface area based on YUV space greedy algorithm selection and slope detection expansion is proposed. Firstly, the RGB rail image is converted to YUV space, and its V component is extracted to reduce the interference of ambient light and noise. Secondly, the grayscale projection inversion curve of the V component is drawn, and the gray mean and median of the curve are used to divide the candidate orbital intervals. Then, greedy algorithm is used to calculate the interval of the maximum suborder sum in the divided curve for rough extraction of rail surface. At last, the slope detection expansion method is used to accurately extract the rail surface, the slope detection at a certain distance is carried out on both sides of the coarse-extracted boundary, and the rail surface boundary is updated with the middle position where the deflection angle is greater than the set threshold. Experimental results show that the proposed method can accurately and rapidly extract the rail surface area, with an average precision of 0.9296, an average accuracy of 96.67%, and an average time of 25.96 ms, which is of certain practical value.

Keywords

• |rail surface area|yuv space|grayscale projection|greedy algorithm|slope detection expansion