DMI cab signal automatic identification based on Cluster Analysis and SVM Algorithm
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摘要: 本文针对CTCS-3级列车控制系统车载设备DMI机车信号基本特征及识别的特点,提出一种基于综合聚类分析和SVM算法的DMI机车信号自动识别方法。建立DMI机车信号模型,通过图像二值化等处理技术提取其目标特征,通过聚类分析对机车信号图像进行初步分类,利用SVM分类器对初步分类后的各种不同机车信号进行目标识别,为进一步实现DMI机车信号的自动数据采集提供了方法支持。试验结果表明,该方法具有良好的性能,机车信号图像的平均识别率达到了95%左右。Abstract: In this paper, according to the characteristics of basic feature and identification about onboard equipment DMI cab signals of CTCS-3 level Train Control System, a novel method based on Cluster Analysis and SVM Algorithm was proposed for DMI cab signal automatic identification. The paper established a model of cab signal, extracted the target features by binary image processing techniques, classified cab signal image preliminary cluster analysis made target identification for various cab signals by SVM classifier, provided a method supporting for further automatic data collection of DMI cab signals. Results showed that the novel method was with good performance. The average image recognition rate was reached about 95%.
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Key words:
- cab signal /
- data mining /
- feature extraction /
- Cluster Analysis /
- SVM Alogrithm
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[1] 何 港.图像识别技术在调车安全中的应用[J].铁道机车与动车,2013（7）. [2] 赵志帅.基于彩色图像处理的铁路信号灯识别及测距技术研究[D].武汉：武汉理工大学,2010. [3] Cortes C,Vpnik V. Support vector networks [J].Machine Learning ,1995,20（3） . [4] 高 锦.基于SVM的图像分类[D].西安：西北大学,2010. [5] 史 峰,王小川,郁 磊,李 洋.Matlab神经网络30个案例分析[M].北京：北京航空航天大学出版社,2010.
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