利用梯度信息和模板匹配进行钢轨和道岔检测

2013 IEEE International Conference on Intelligent Rail Transportation Proceedings Pub Date : 2013-08-01 DOI:10.1109/ICIRT.2013.6696299

Jorge Corsino Espino, B. Stanciulescu, Philippe Forin

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引用次数: 7

摘要

提出了一种不基于经验阈值的轨道道岔检测算法。轨道提取是基于利用滚动垫的宽度进行边缘检测。然后，这个边缘检测方案被用作RANSAC算法的输入，以确定轨道的模型。道岔检测方案基于定向梯度直方图(HOG)和模板匹配(TM)。结果表明:(i)我们的铁路轨道提取方案具有可靠的性能，(ii)使用支持向量机(SVM)分类器的道岔检测方案的正确率为97.31%。

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Rail and turnout detection using gradient information and template matching

This paper presents a railway track and turnout detection algorithm which is not based on an empirical threshold. The railway track extraction is based on an edge detection using the width of the rolling pads. This edge detection scheme is then used as an input to the RANSAC algorithm to determine the model of the rails. The turnout detection scheme is based on the Histogram of Oriented Gradient (HOG) and Template Matching (TM). The results show (i) reliable performance for our railway track extraction scheme and (ii) a correction rate of 97.31 percent for the turnout detection scheme using a Support Vector Machine (SVM) classifier.

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2013 IEEE International Conference on Intelligent Rail Transportation Proceedings

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