Study of Auto Rack Girder Detection System
On account of out-of-date and low efficiency detection mode for large dimension auto rack girder, it is presented that is the Machine Vision on-line detection system for auto rack girder Based on RBF and linear structured light. It is corrected according to the error of RBF neural network for camera lens. Taken advantage of RBF characteristic that approximating high order input and output nonlinear system, camera image error correction high order distortion model is constructed and the precision of camera detection is raised. This correction method is used in China FAYV Group Corporation for auto rack girder detection system. Automatic non-contact detection is firstly realized in China for auto rack girder holes diameter, position and number. After tested at production field, the precision of detection diameter is ± 0.1mm, the precision of detection position is ± 0.3mm, and the detection time is less than 1.5 minutes. It is proved in practice that the method of linear array CCD camera lens distortion correction based on RBF neural network is feasible.
linear structured light RBF neural network linear array CCD auto rack girder
Hua wang Jingang Gao Shuang Zhang
College of Mechanical Science and Engineering Changchun Institute of Technology Changchun 130012,China
国际会议
长春
英文
547-550
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）