Coarse-to-Fine Adaptive Illumination Hard-Adjustment for Vision Inspection System Under Uncertain Imaging Conditions

Fei Chang, Yunqiang Duan, Min Liu, Mingyu Dong
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Abstract

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.
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不确定成像条件下视觉检测系统粗精自适应照度硬调节
在不确定的成像条件下(如光照不均匀和变化、不同视点和不同物体距离等)获取高质量图像是一项非常具有挑战性的任务。然而,工业视觉检测系统的成像质量对后续的图像处理至关重要,特别是对于那些具有挑战性的检测任务,如车身表面的微小缺陷检测。为了克服成像条件不确定给图像采集带来的挑战,提出了一种两阶段自适应照度调整方法,以处理光照、视点和目标距离的多样性所带来的不确定性。我们的算法框架已经实现并应用于某汽车喷漆厂的车身表面微小缺陷检测移动检测系统中。实际工业应用验证了该方法的有效性和高效性。因此,本文提出的从粗到精的框架可以看作是不确定成像条件下工业视觉检测系统的自适应硬调整方案。
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