Fast detection study of foreign object intrusion on railway track

Hongxia NIU; Tao HOU

doi:10.5604/01.3001.0012.6510

Authors

Hongxia NIU Automatic Control Institute, Lanzhou Jiaotong University, Lanzhou, China Author
Tao HOU School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, China Author

DOI:

https://doi.org/10.5604/01.3001.0012.6510

Keywords:

railway track, foreign object detection, multi-background modeling, multiple difference

Abstract

The foreign objects intrusion on railway track has seriously affected the safe operation of the train, and it is extremely urgent to monitor them in real time. In order to improve the detection accuracy and rapidity of foreign objects intrusion on railway track, the new detection method of foreign object intrusion on railway track based on multi-background modeling, multi-difference and proportion method of black and white pixels is put forward in this paper. The multi-background modeling method that includes the historical background modeling, the multi-frame average background modeling and the previous frame of current frame background modeling method is used to model background modeling, and the three backgrounds are updated respectively to achieve background updating. The improved Canny method and Hough transform method is used to extract track edge, and get the final track edge image. Based on track edge image, the railway track dangerous area was established through the image segmentation method to reduce the amount of information in image processing and improve the processing speed. And then, according to the structure method of multi-background modeling, the detection method that fuses the historical background difference, average background difference and interframe difference is used to detect foreign object intrusion on track, and the detection result was processed by the morphological open processing. Finally, for the foreign objects intrusion, the decision is done by the quantitative proportion method of black and white pixels of image. The experimental results show that this method has better noise immunity performance and environmental adaptability, and the accuracy and rapidity of foreign objects intrusion detection is improved effectively.

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