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The main task of vision-based industrial defect inspection is to implement efficient non-contact visual quality control, i.e., to detect if there is a defect and to achieve an accurate 3D shape measurement of such a defect, and this kind of vision defect inspection system has been widely applied in various industrial application. However, it is still not the case in the inspection of transparent microdefect on the polarizer (which is the most important part of an LCD screen). Optical measurement devices (such as confocal microscopy) are often utilized to fulfil this task. To solve problems lied in the current confocal microscopy inspection system, such as expensive and non-real-time processing, this research aims to develop a novel vision-based 3D shape measurement system for polarizer transparent microdefect characterization. The innovation of this system, which has been verified by our optical model simulation, is that the 3D sizes of microdefect have a monotonically relation to the grayscale of the microdefect image. Hence, a microdefect imaging system, which could acquire defect image accurately, is first well designed and implemented. Then, a support vector regression (SVR) algorithm is derived by the trained data, i.e., 100 acquired defect images and its corresponding 3D shape value by confocal microscopy. Characterized 3D measurement of microdefect is thereby obtained by this SVR algorithm. 30 polarizer microdefect samples have been imaged and measured by our proposed system, and several important performance indicators, including processing speed, accuracy and system reproducibility, have been elaborately tested. The experimental results show that the proposed system could achieve a high-accuracy measurement but in a much faster and more efficient way than the confocal microscopy. Besides, this developed imaging system has been evaluated in real applications, and over 300 samples have been detected, which also validate the effectiveness of the proposed system.

Machine vision systems have been widely used in industrial production lines because of their automation and contactless inspection mode. In polymeric polarizers, extremely slight transparent aesthetic defects are difficult to detect and characterize through conventional illumination. To inspect such defects rapidly and accurately, a saturated imaging technique was proposed, which innovatively uses the characteristics of saturated light in imaging by adjusting the light intensity, exposure time, and camera gain. An optical model of defect was established to explain the theory by simulation. Based on the optimum experimental conditions, active two-step scanning was conducted to demonstrate the feasibility of this detection scheme, and the proposed method was found to be efficient for real-time and in situ inspection of defects in polymer films and products.

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The quality-control process of polarizer production is hampered by the presence of extremely-slight transparent aesthetic defects (ESTADs). The saturated imaging method based on stripe structured backlight can effectively improve the imaging contrast of ESTADs. However, the contrast is very sensitive to the saturation degree, which requires careful manual selection. This paper presents a saturation level-guided image enhancement method that is simple to deploy in industrial settings. First, a new definition of the saturation level for structured backlit imaging with translation, scale, and rotation invariance is proposed. Then, an empirical model of contrast versus saturation level is established. Using the contrast data measured at five saturation levels, the optimal saturation level can be estimated using the parameter optimization method. The experimental results demonstrate that the method is effective, easy to use, and an improvement of imaging effects for transparent thin-film defect detection algorithms.