|Table of Contents|

Fast recognition method of rail region based on hue value mutation feature of image(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2016年01期
Page:
46-54
Research Field:
道路与铁道工程
Publishing date:

Info

Title:
Fast recognition method of rail region based on hue value mutation feature of image
Author(s):
MIN Yong-zhi YIN Chao DANG Jian-wu CHENG Tian-dong
School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
Keywords:
track inspection rail region machine vision region recognition hue value mutation linear fitting recognition time recognition failure rate
PACS:
U216
DOI:
-
Abstract:
The rail boundary points in track inspection image were extracted by detecting the hue value mutation features of different regions in the HSL color space of color images. The rail's edges were determined by the linear fitting of multiple rail boundary points of different bisectors, and then the target rail region was recognized. The distribution features of sleeper, ballast, fastener and rail and the hue value mutation features of different characteristic regions were analyzed in the captured sequence images of track inspection system using machine vision. The correspondences between the hue value mutation point and the rail boundary points of different bisectors under different numbers of equal parts were researched. The influences of different numbers of equal parts on recognition time and recognition failure rate were discussed. The recognition method was compared with the traditional method under different light conditions. Analysis result indicates that when the number of equal parts is 8, the optimal recognition effect is obtained, and the recognition failure rate is 5.0%, the recognition time is 4.65 ms. In the three intervals of characteristic light intensity, such as 500-1 000, 1 000-10 000, and 10 000-100 000 lx, the average maximum recognition times of recognition method in the rail regions of tracks with wood sleepers and concrete sleepers are 4.57 ms and 4.48 ms respectively, and are 44.4% and 47.1% less than the values of traditional method respectively. The standard deviations of recognition times are 0.15 ms and 0.12 ms respectively, and are 91.8% and 93.6% lower than the values of traditional method respectively. The average biggest recognition failure rates of proposed method are 3.5% and 3.3% respectively, and are 66.0% and 76.9% less than the values of traditional method respectively. The standard deviations of recognition failure rate are 1.6%, and are 68.9% and 71.1% lower than the values of traditional method respectively. So the proposed method is an effective recognition method of target rail region in the track inspection system using machine vision. 10 figs, 25 refs.

References:

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Last Update: 2016-02-20