Abstract
The idea of recursion and limited range is introduced in the traditional fast binarization algorithm. Firstly, according to the characteristics of the fast binarization algorithm, the recursion formulas of four parameters are deduced, and the complexity of the image is calculated . Then fast threshold segmentation of image is finished within the reduced gray level range. Because of considering the four parameters formula of recursive and fully aware of the images complexity, the gray value of the image to be traversed is greatly reduced, and the redundancy of the algorithm is reduced. Finally, the improved algorithm is applied to extraction of railway track obstacle. The experimental results show that this algorithm complexity is lower than traditional algorithm and Otsu, and the computation speed can be improved by about 60%. It can meet the real-time requirement for railway track obstacle image segmentation, and the segmentation effect is almost the same as the traditional one.
Highlights
The infrastructure, train running status and the collection, transmission and real-time processing of environment information are the key to the safety of high speed railway
In view of this situation, this paper presents an improved fast binarization algorithm, the algorithm fully considers the characteristics of the original image itself while the algorithm improves the traditional fast binarization algorithm repeatability of the parameter calculation formula, thereby reducing the computational redundancy, improving the real-time of the algorithm
The traditional fast binarization algorithm and the improved algorithm in this paper is equal to the two methods, the segmentation effect is the same
Summary
The infrastructure, train running status and the collection, transmission and real-time processing of environment information are the key to the safety of high speed railway. How to extract railway obstacles using machine vision instead of human eyes, and have the feature extraction and accurate recognition, is a new trend to reduce the occurrence rate of railway accidents and to adapt to the railway operation[1]. Dong Zhongyan et al[8] proposed an improved algorithm based on image complexity, which can save memory and improve computing speed by about 40% on the basis of ensuring the segmentation effect In view of this situation, this paper presents an improved fast binarization algorithm, the algorithm fully considers the characteristics of the original image itself while the algorithm improves the traditional fast binarization algorithm repeatability of the parameter calculation formula, thereby reducing the computational redundancy, improving the real-time of the algorithm. The improved algorithm is applied to the extraction of obstacle on railway track
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