Subway brake shoe fault detection method based on three-dimensional point cloud
A technology of fault detection and 3D point cloud, applied in neural learning methods, details related to processing steps, image data processing, etc., can solve problems such as loss of subway traffic safety hazards, improve comprehensiveness and reliability, and ensure operation safety , the effect of ensuring robustness and measurement accuracy
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[0044] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific implementation methods.
[0045] The method of data collection in the present invention is that the intelligent inspection trolley is accurately positioned at the designated collection location, and the three-dimensional point cloud data (XYZ) and color information (RGB) of the corresponding parts at the bottom of the subway car are collected by the three-dimensional industrial camera mounted on the robotic arm of the trolley. Several steps are required to realize the fault detection of the brake shoe. It needs to train the segmentation model, realize preprocessing, point cloud segmentation, point cloud processing, parameter calculation and other steps to complete the entire algorithm. By calculating the thickness of the brake shoe, it is possible to accurately judge whether there is wear or not, which is more accurate and reliable than the naked eye...
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