Efficient detection method for rebar spacing of prefabricated components based on RGBD images
To ensure the production quality of prefabricated components and the smooth installation on site,it is usually necessary to detect the spacing of steel bars before leaving the factory.Current rebar spacing detection primarily rely on manual methods,which are time-consuming and labor-intensive.To improve the efficiency of rebar spacing detection,an efficient detection method of rebar spacing of prefabricated components based on RGBD image was proposed.Based on the color image collected by the depth camera,the semantic segmentation neural network was used to achieve efficient segmentation of rebar pixels.The point cloud was generated by using the internal parameters of the camera,and the point cloud of the rebar was accurately segmented according to the steel bar pixels and the data was enhanced based on the point cloud features.The point cloud processing algorithm was used to realize the efficient detection for rebar spacing of prefabricated components.In the verification test,the rebar spacing of a precast concrete slab unit containing 272 rebar was tested.The results show that the proposed detection method can accurately and efficiently complete the detection of rebar spacing of prefabricated components,and has significant practical value and economic benefits.
RGBD imagesemantic segmentationpoint cloud datarebar spacingefficient detectiondepth camera