Automatic measurement of structural steel bar size based on ArUco assisted stitching and lightweight semantic segmentation network
To address the issue of large rebar dimensions that cannot be covered by a single camera field of view in processing and inspection,a method for measuring the geometric shape(length and angle)of rebar using computer vision technology was proposeed.A partitioned camera measurement system was proposed.The partitioned cameras were utilized to simultaneously capture images and stitch together a panoramic view based on binary encoded ArUco markers.For real-time computing requirements,a lightweight semantic segmentation network,named as ghost-Unet,was proposed to balance the efficiency and accuracy of rebar contour recognition.To ensure computational accuracy,a robust straight-line detection algorithm was utilized for segmenting and fitting the rebar contours.The measurement results indicate that in the laboratory,the average length measurement error is 2.92 mm when measuring vertical rebar with the optical axis of camera perpendicular to the plane.The average length measurement error is 4.27 mm when measuring horizontal rebar with the optical axis of camera at an angle much less than 90° to the plane due to a rebar thickening issue in the orthographic correction stage.In the field,the average length measurement error is 3.08 mm,and the angle error is within 0.7°.
construction quality inspectionmeasurement of steel bar sizeimage stitchingcomputer visionlightweight semantic segmentation
万方数据
维普
