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
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.
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.
Fig.3Picture 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.18 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.2Average weight value of characteristic sample data
Fig.4Results of thirty runs of ReliefF algorithm
Fig.5Clustering 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.3Membership 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.4Experimental 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.5Experimental comparison results of different clustering algorithms
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