Intelligent detection method for steel reinforcement skeleton size based on depth camera
The quality inspection of the reinforced skeleton plays an important role in the production process of prefabricated reinforced concrete components,because its quality directly affects the performance and reliability of the whole component.At present,the quality inspection of rebar skeletons mainly relies on traditional manual means,such as the use of steel ruler measurement and manual counting to evaluate key indicators such as the number and spacing of rebar spacing.However,this approach has significant limitations such as inefficiency,high cost,and error-proneness.In order to solve the above problems,this paper introduces depth camera sensor and advanced technology for the multi-layer reinforcement skeleton in the prefabricated reinforced concrete components,and designs a novel quality inspection method for the reinforcement skeleton,that is,the image is collected by the depth camera,the double-layer reinforcement is converted into a single-layer image according to the depth threshold screening algorithm,and then the intersection coordinates are detected by the improved YOLOv5 algorithm,and finally the binding spacing is solved according to the coordinate conversion.Experimental results show that the proposed method improves the accuracy of inspection,reduces time consumption,and reduces the dependence on manual labor through intelligent algorithms.
measurement of steel bar binding spacingdepth cameraYOLOv5 algorithmcomputer visionvisual measurements
万方数据
维普
