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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.
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Received: 18 February 2020
Published: 25 April 2021
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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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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
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