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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (3): 586-593    DOI: 10.3785/j.issn.1008-973X.2021.03.020
    
Metro location point matching and false alarm elimination based on FCM algorithm of target image
Ying-jie ZHENG1,2(),Song-rong WU1,2,Ruo-yu WEI1,2,Zhen-wei TU1,2,Jin LIAO3,Dong LIU1,*()
1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
2. Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, Southwest Jiaotong University, Chengdu 611756, China
3. Sichuan Juzhi Jingchuang Rail Transit Technology Co. Ltd, Chengdu 610000, China
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Abstract  

A fuzzy C-means (FCM) algorithm was used to match the location point model in view of the false alarm problem of the track location point detection model, in the process of accurate and fast positioning of metro track based on image recognition. On the basis of the detection model of track location points based on the deep convolution neural network, six kinds of location point images and two kinds of false alarm point images was selected, and then the features (six dimensional feature quantities of each image) such as the center relative position, length width ratio and area of each target detection frame in different types of image samples was extracted. The ReliefF algorithm was adopted to measure the weight value of each dimension feature of all image samples, which was introduced into the Euclidean distance formula of FCM algorithm, so as to uniquely match the location points. Results indicate that the improved FCM algorithm has an obvious improvement in the correctness and effectiveness of clustering, which is of great significance to enhance the accuracy of metro track positioning.

Key words： image recognition      deep convolutional neural network      track locating points      fuzzy C-means algorithm      feature weighting     
Received: 18 February 2020      Published: 25 April 2021
CLC:  TP 391.44  
  U 231.94  
Fund:  国家自然科学基金资助项目（61531016）；四川省重大科技专项资助项目（20QYCX0095）
Corresponding Authors: Dong LIU     E-mail: 2280511301@qq.com;liudong@swjtu.edu.cn
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Ying-jie ZHENG
Song-rong WU
Ruo-yu WEI
Zhen-wei TU
Jin LIAO
Dong LIU
Cite this article:

Ying-jie ZHENG,Song-rong WU,Ruo-yu WEI,Zhen-wei TU,Jin LIAO,Dong LIU. Metro location point matching and false alarm elimination based on FCM algorithm of target image. Journal of ZheJiang University (Engineering Science), 2021, 55(3): 586-593.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.03.020     OR     http://www.zjujournals.com/eng/Y2021/V55/I3/586


基于目标图像FCM算法的地铁定位点匹配及误报排除方法

在基于图像识别的地铁轨道精确快速定位过程中，针对轨道定位点检测模型存在的误报问题，将模糊C均值（FCM）算法用于定位点模型匹配. 在基于深度卷积神经网络的轨道定位点检测模型基础上，选用6类定位点图像和2类误报点图像，提取不同类别图像样本中各目标检测框的中心相对位置、长宽比、面积等特征数据（每张图像各6维特征量），采用ReliefF算法度量所有图像样本各维特征量的权重，将所得权重引入FCM算法的欧几里德距离公式，匹配唯一定位点. 实验结果表明，改进后的FCM算法在聚类的正确性和有效性方面有明显改善，对提高地铁轨道定位精度具有重要的意义.


关键词： 图像识别,  深度卷积神经网络,  轨道定位点,  模糊C均值算法,  特征加权 
Fig.1 Target detection process of Darknet-53 model
Fig.2 Flow chart of track location point detection
Fig.3 Picture of location points and false alarm points
样本类别 x y L1 L2 S1 S2
1 1 322 59 0.888 3 0.892 2 150 792 37 128
2 654 ?108 2.848 1 2.233 6 71 100 102 292
3 252 20 0.518 3 0.582 1 98 536 94 068
4 14 ?305 2.837 8 2.824 1 139 860 131 760
5 71 396 0.970 9 0.410 1 41 200 77 252
6 249 379 1.985 5 0.590 2 37 812 140 544
7 72 ?432 0.597 2 3.571 4 49 536 70 000
8 365 90 0.973 3 0.291 1 21 900 52 824
Tab.1 8 sets of standard feature sample data
x y L1 L2 S1 S2
0.241 1 0.291 0 0.286 5 0.254 5 0.260 4 0.225 1
Tab.2 Average weight value of characteristic sample data
Fig.4 Results of thirty runs of ReliefF algorithm
Fig.5 Clustering results of two FCM algorithms
样本类别 u31 u32 u33 u34 u35 u36 u37 u38 u39 u40 u61 u62 u63 u64 u65 u66
1 0.000 8 0.001 2 0.000 3 0.001 0 0.002 4 0.001 7 0.001 5 0.001 9 0.000 2 0.000 6 0.155 2 0.148 3 0.155 0 0.148 0 0.149 5 0.151 1
2 0.001 4 0.001 9 0.000 5 0.001 7 0.004 1 0.003 0 0.002 4 0.003 1 0.000 3 0.001 0 0.148 7 0.152 7 0.152 2 0.149 7 0.149 7 0.148 7
3 0.000 8 0.001 2 0.000 3 0.001 0 0.002 4 0.001 7 0.001 4 0.001 9 0.000 2 0.000 6 0.224 3 0.227 0 0.235 9 0.214 4 0.217 9 0.215 5
4 0.001 1 0.001 6 0.000 4 0.001 3 0.003 3 0.002 2 0.001 8 0.002 5 0.000 2 0.000 7 0.127 4 0.130 2 0.130 3 0.128 0 0.129 3 0.127 3
5 0.096 1 0.094 8 0.058 6 0.152 9 0.193 8 0.716 1 0.255 8 0.258 2 0.030 7 0.920 9 0.083 1 0.083 5 0.078 4 0.088 1 0.086 2 0.086 8
6 0.003 1 0.004 7 0.001 2 0.003 7 0.008 9 0.005 6 0.005 7 0.007 7 0.000 6 0.001 9 0.104 4 0.103 4 0.099 8 0.107 9 0.106 7 0.107 4
7 0.895 8 0.893 6 0.938 3 0.837 4 0.783 0 0.268 1 0.730 0 0.722 8 0.967 8 0.073 7 0.076 8 0.076 9 0.072 5 0.081 1 0.079 5 0.080 1
8 0.000 8 0.001 1 0.000 3 0.001 0 0.002 2 0.001 7 0.001 4 0.001 9 0.000 2 0.000 6 0.080 3 0.078 1 0.075 9 0.082 7 0.081 3 0.083 0
Tab.3 Membership function values of FCM algorithm samples 31~40 and 61~66
算法 CA/% RI
FCM 91.67 0.97
改进的FCM 100.00 1.00
Tab.4 Experimental comparison results of the two FCM algorithms
聚类算法 CA/% RI
K-means算法 77.78 0.93
层次聚类法 91.67 0.89
Tab.5 Experimental comparison results of different clustering algorithms
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