Citation

Chen WY, Wang M, Fu ZX. Sensors (Basel) 2014; 14(6): 10578-10597.

Affiliation

College of Electric and Control Engineering, Xi'an University of Science and Technology, 58 Yan-Ta Road, Xi'an City 710054, Shaanxi Province, China. fuzx@xust.edu.cn.

Copyright

(Copyright © 2014, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s140610578

PMID

24936948

Abstract

Most railway accidents happen at railway crossings. Therefore, how to detect humans or objects present in the risk area of a railway crossing and thus prevent accidents are important tasks. In this paper, three strategies are used to detect the risk area of a railway crossing: (1) we use a terrain drop compensation (TDC) technique to solve the problem of the concavity of railway crossings; (2) we use a linear regression technique to predict the position and length of an object from image processing; (3) we have developed a novel strategy called calculating local maximum Y-coordinate object points (CLMYOP) to obtain the ground points of the object. In addition, image preprocessing is also applied to filter out the noise and successfully improve the object detection. From the experimental results, it is demonstrated that our scheme is an effective and corrective method for the detection of railway crossing risk areas.

Language: en