Geometric imperfection detection method of chord pipe in transmission tower and application based on machine vision

The bearing capacity of the transmission tower can be decreased by the geometric imperfections of chord members. Because the chord members are large and they are welded with foot nails and gusset plates, it is difficult to test their imperfection by the primitive tool such as calipers. Therefore, firstly, the point clouds of the chord members are collected by lase radar; secondly the pipe of the chord member is detected by the deep learning method, then the initial curvature, ellipticity and local unsmooth are calculated; Finally, 5 chord members were tested, and the visual results were given out. The results shows: 1) The ellipticity and local unsmooth of the pipes are not large, except the vicinity of the weld lines; 2) the initial curvature is large, and some pipes has exceed 0.1% length, which may lead to a lower bearing capacity than that calculated by code. Therefore, it is proposed that the bearing capacity can be calculated by the imperfections tested for the important transmission towers