Haixiang Xu, Zhengqiu Huang, Jun Qiu, Wen Gong, Fengqi Wu
{"title":"轨道测量装置激光光斑中心定位","authors":"Haixiang Xu, Zhengqiu Huang, Jun Qiu, Wen Gong, Fengqi Wu","doi":"10.1145/3514105.3514128","DOIUrl":null,"url":null,"abstract":"On the basis of the existing rail laser measuring device, the paper proposes a method of laser spot center calculation that is suit for the in-site survey base on the common algorithms’ comparison. And the morphological cascade filter and Gaussian filter are constructed in the denoising and smoothing pre-processing of the laser spot image. In order to make full use of the intensity distribution of laser spot, the threshold range is determined by the Otsu algorithm and maximum light intensity, which is non-uniformly segmented by exponential function to obtain a series of binary spot images. After the center of light spot is initially determined by the centroid method, a series of center samples are obtained by Hough transform on the edge extraction image, and then the clustering center is obtained by K-means algorithm, at last the actual coordinate of the laser spot center is obtained through the relationship of the coordinate functions fitted by BP neural network. The proposed algorithm is tested on the coordinate screen of the track trolley, and its error can meet the requirements of the in-site survey.","PeriodicalId":360718,"journal":{"name":"Proceedings of the 2022 9th International Conference on Wireless Communication and Sensor Networks","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Laser Spot Center Location of Track Measuring Device\",\"authors\":\"Haixiang Xu, Zhengqiu Huang, Jun Qiu, Wen Gong, Fengqi Wu\",\"doi\":\"10.1145/3514105.3514128\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"On the basis of the existing rail laser measuring device, the paper proposes a method of laser spot center calculation that is suit for the in-site survey base on the common algorithms’ comparison. And the morphological cascade filter and Gaussian filter are constructed in the denoising and smoothing pre-processing of the laser spot image. In order to make full use of the intensity distribution of laser spot, the threshold range is determined by the Otsu algorithm and maximum light intensity, which is non-uniformly segmented by exponential function to obtain a series of binary spot images. After the center of light spot is initially determined by the centroid method, a series of center samples are obtained by Hough transform on the edge extraction image, and then the clustering center is obtained by K-means algorithm, at last the actual coordinate of the laser spot center is obtained through the relationship of the coordinate functions fitted by BP neural network. The proposed algorithm is tested on the coordinate screen of the track trolley, and its error can meet the requirements of the in-site survey.\",\"PeriodicalId\":360718,\"journal\":{\"name\":\"Proceedings of the 2022 9th International Conference on Wireless Communication and Sensor Networks\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 9th International Conference on Wireless Communication and Sensor Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3514105.3514128\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 9th International Conference on Wireless Communication and Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3514105.3514128","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Laser Spot Center Location of Track Measuring Device
On the basis of the existing rail laser measuring device, the paper proposes a method of laser spot center calculation that is suit for the in-site survey base on the common algorithms’ comparison. And the morphological cascade filter and Gaussian filter are constructed in the denoising and smoothing pre-processing of the laser spot image. In order to make full use of the intensity distribution of laser spot, the threshold range is determined by the Otsu algorithm and maximum light intensity, which is non-uniformly segmented by exponential function to obtain a series of binary spot images. After the center of light spot is initially determined by the centroid method, a series of center samples are obtained by Hough transform on the edge extraction image, and then the clustering center is obtained by K-means algorithm, at last the actual coordinate of the laser spot center is obtained through the relationship of the coordinate functions fitted by BP neural network. The proposed algorithm is tested on the coordinate screen of the track trolley, and its error can meet the requirements of the in-site survey.