In order to realize the rapid detection of three-dimensional defects of connectors, this paper proposes a method for detecting connector defects based on structured light. This method combines structured light with bi...In order to realize the rapid detection of three-dimensional defects of connectors, this paper proposes a method for detecting connector defects based on structured light. This method combines structured light with binocular stereo vision to obtain three-dimensional data for the connector. Point cloud registration is used to identify defects and decision trees are used to classify defects. The accuracy of the 3D reconstruction results in this paper is 0.01 mm, the registration accuracy of the point cloud reaches the sub-millimeter level, and the final defect classification accuracy is 94%. The experimental results prove the effectiveness of the proposed three-dimensional connector defect detection method in connector defect detection and classification.展开更多
基金This work has been supported by the National Natural Science Foundation of China(Nos.61078041 and 51806150)the Natural Science Foundation of Tianjin(Nos.16JCYBJC15400,15JCYBJC51700 and 18JCQNJC04400)+2 种基金the State Key Laboratory of Precision Measuring Technology and Instruments(Tianjin University)the Program for Innovative Research Team in University of Tianjin(No.TD13-5036)the Tianjin Enterprise Science and Technology Commissioner Project(No.18JCTPJC61700)。
文摘In order to realize the rapid detection of three-dimensional defects of connectors, this paper proposes a method for detecting connector defects based on structured light. This method combines structured light with binocular stereo vision to obtain three-dimensional data for the connector. Point cloud registration is used to identify defects and decision trees are used to classify defects. The accuracy of the 3D reconstruction results in this paper is 0.01 mm, the registration accuracy of the point cloud reaches the sub-millimeter level, and the final defect classification accuracy is 94%. The experimental results prove the effectiveness of the proposed three-dimensional connector defect detection method in connector defect detection and classification.
