SIFT算法在机器视觉轨道交通变形监测中的应用分析

Application and analysis of SIFT algorithm in machine vision deformation monitoring of rail transit

针对传统人工测量方法不能满足轨道交通结构变形监测精度要求高、实时性要求强等问题,本文提出一种尺度不变特征变换(Scale-Invariant Feature Transform,SIFT)算法,基于该算法可实现机器视觉对轨道交通结构变形实时监测,同时基于MATLAB语言构建机器视觉轨道交通变形监测系统,并对该系统监测结果进行精度评估。结果表明,基于SIFT算法构建的机器视觉轨道交通变形监测系统与传统测量结果有较好的一致性,总体精度优于1mm。总之,SIFT算法可较好地匹配特征点信息,能够用于实现机器视觉轨道交通结构变形监测,同时机器视觉轨道交通结构变形监测系统在自动化程度、实时性、成本方面具有显著优势,拥有广阔的应用前景。

Aiming at the problem that the traditional manual measurement method cannot meet the requirements of high precision and real-time for the deformation monitoring of rail transit structures,this paper proposes a Scale-Invariant Feature Transform(SIFT)algorithm.Based on this algorithm,the realtime monitoring of rail transit structure deformation is realized by machine vision.At the same time,a machine vision rail transit deformation monitoring system is built based on MATLAB language.The results show that the machine vision rail transit deformation monitoring system based on SIFT algorithm has good consistency with the traditional measurement results,and the overall accuracy is better than 1mm.In a word,SIFT algorithm can better match feature point information,and can be used to realize machine vision rail transit structure deformation monitoring.At the same time,machine vision rail transit structure deformation monitoring system has significant advantages in automation,real-time and cost,and has broad application prospects.