Automated inspection approach for OCA particles in multi-layered cover glass of display module assembly

CG (Cover Glass) is a critical component of electronic screens, and its manufacturing quality is closely relevant to the display effect. To satisfy the requirement of quality control, a machine vision system for real-time inspection of particles in the OCA (Optically Clear Adhesive) layer of CG is presented in this paper. With a brief description of the optical logic of particle imaging and the design of the vision system, our emphasis is put on the post-processing image analysis. To align particles regions in the CG under multi-mode imaging, a spatial alignment calibration algorithm is proposed with perspective distortion correction to calculate the triaxial offset. Then, a CLAHE+PM filtering is adopted to enhance the contrast of the particle. Furthermore, a Meanshift method combined with adaptive local thresholding is proposed to extract the contours of tiny particles. Finally, to distinguish between multiple layers of particles in the CG and detect OCA particles, a combination of fast background reconstruction and Averaged Stochastic Gradient Descent-Support Vector Machine is used. According to in-line experiments and tests, our system can find out a majority of the OCA particles with a P R (over-detection rate) of 1.31% and a P M (miss detection rate) of 0.33% for over 10 000 CG samples.