WANG Xuekun, YU Wenwen, HONG Cuncun, CAO Jianjun, QIAN Weiying, GAO Shumei. Machine vision on-line detecting system for effectively eliminating non-detection zone of surface defects[J]. Journal of Applied Optics, 2020, 41(6): 1190-1196. DOI: 10.5768/JAO202041.0602002
Citation: WANG Xuekun, YU Wenwen, HONG Cuncun, CAO Jianjun, QIAN Weiying, GAO Shumei. Machine vision on-line detecting system for effectively eliminating non-detection zone of surface defects[J]. Journal of Applied Optics, 2020, 41(6): 1190-1196. DOI: 10.5768/JAO202041.0602002

Machine vision on-line detecting system for effectively eliminating non-detection zone of surface defects

  • For leather surface defects with the machine vision on-line detection, the image quality of some leather surface defects presented aeolotropism to the lighting direction, and the defects even failed to be shown, therefore the non-detection zone was formed. Considering this problem, an on-line detecting system which could eliminate the non-detection zone was designed. Based on the scattering and the information acquisition mechanism of the defect, the change law of the defect feature light intensity with the lighting direction was obtained. An experimental study on the defects of imprinting and ink was carried out to show that these two types of defect feature light intensity distributions were related to the lighting direction, and the non-detection zone as well as the effective zone of defects presented a orthogonal state. According to this, a self-designed off-axis plane light source with side-entering uniform irradiation was adapted to design the on-line detecting system for the orthogonal composition with linear array and planar array. The vertical target plane was used to collect the information and the dark field orthogonal lighting was carried out at 45°. The main detecting system was composed of the linear-array camera and the linear light source, while the auxiliary detecting system was composed of the planar camera and the off-axis plane light source to detect hundreds of samples of random anisotropic defects. The experimental results show that the defects non-detection zone is effectively eliminated, the defects recognition rate is increased by 22%, and the detection effciency is also greatly improved.
  • loading

Catalog

    Turn off MathJax
    Article Contents

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return