Displacement monitoring method based on artificial intelligence and multi-view vision and related device

Abstract

The invention relates to the field of artificial intelligence, and discloses a displacement monitoring method based on artificial intelligence and multi-view vision and a related device, which are used for improving the precision of displacement monitoring. The method comprises the following steps: respectively carrying out feature fusion and feature matching on a plurality of tunnel feature points to obtain a plurality of feature point pairs, obtaining feature point coordinates of each feature point pair, and calculating attribute parameters of a visual displacement analyzer according to the feature point coordinates of each feature point pair; performing three-dimensional mapping on the gas transmission pipeline tunnel according to the attribute parameters and the plurality of detection areas to obtain initial coordinate information, and performing coordinate conversion on the initial coordinate information to obtain first coordinate information; generating second coordinate information according to the second tunnel image, and calculating displacement and vibration frequency data according to the first coordinate information and the second coordinate information; and generating a displacement frequency curve according to the displacement and vibration frequency data, and performing safety monitoring on the gas transmission pipeline tunnel according to the displacement frequency curve to obtain a safety monitoring result. 本发明涉及人工智能领域，公开了一种基于人工智能与多目视觉的位移监测方法及相关装置，用于提高位移监测的精度。所述方法包括：分别对多个隧道特征点进行特征融合和特征匹配，得到多个特征点对，并获取每个特征点对的特征点坐标，根据每个特征点对的特征点坐标计算视觉位移分析仪的属性参数；根据属性参数和多个检测区域对输气管线隧道进行三维映射，得到初始坐标信息，并对初始坐标信息进行坐标转换，得到第一坐标信息；根据第二隧道图像生成第二坐标信息，并根据第一坐标信息和第二坐标信息计算位移及震动频率数据；根据位移及震动频率数据生成位移频率曲线，并根据位移频率曲线对输气管线隧道进行安全监测，得到安全监测结果。