Component Recognition Method of Block Erection Surface Based on Point Cloud Object Detection Algorithm
In terms of the accuracy detection of block erection surface,3D laser scanners have advantages over total stations in terms of efficiency,accuracy,and ease of operation.However,scan-generated point clouds often entail vast amounts of data,frequently including numerous surrounding spatial points unrelated to the design model.This not only extends computational time but also undermines registration accuracy.The point cloud registration method based on salient components effectively addresses this problem.In order to achieve intelligent recognition of salient components,a smart recognition algorithm specific to block erection surface components is required.A deep learning method is adopted to build a point-based,anchor-free,single-stage object detection neural network model that is suitable for block erection surface point cloud data and basically achieves intelligent recognition of components on the block erection surface.The network is optimized and trained using the ADAM optimizer and a PA-m of 64.36％is achieved on the test dataset.The results can be used to improve the coarse registration method of point clouds and provide assistance in achieving intelligent and efficient accuracy detection of block erection surfaces.
万方数据
维普
