Coarse-To-Fine Adaptive Adjustment For Vision Illumination Inspection System Hard-Under Uncertain Imaging Conditions

High-quality image acquisition under uncertain imaging conditions (such as uneven and varied illuminations, various viewpoints and different object distances, etc.) is a very challenging task. However, the imaging quality of industrial vision inspection system is vital to subsequent image processing, especially for those challenging detection tasks, such as tiny defect inspection of paint car-body surfaces. In order to overcome the challenge of image acquisition due to uncertain imaging conditions, a two-stage adaptive illumination adjustment method is proposed to handle the uncertainty caused by diversities of lighting, viewpoint and object distance. Our algorithm framework has been implemented and applied to the mobile inspection system deployed in a car painting factory for tiny defect detection of paint car-body surfaces. The efficiency and effectiveness of our method has been validated by the actual industrial application. As a result, the proposed coarse-to-fine framework can be viewed as an adaptive hard-adjustment solution for industrial vision inspection system under uncertain imaging conditions.