Defect detection and classification for mobile phone cover glass based on visual perception

In order to improve the product quality, defect detection is a significant concern in the production of MPCG (Mobile Phone Cover Glass). Aiming at this problem, an automatic defect detection system is developed in this paper. The system adopts backlight imaging strategy to improve the Signal-to-noise ratio (SNR). With the captured images, an adaptive binarization algorithm is firstly adopted and improved which is based on integral graph to achieve high real-time performance. A multi-dimensional feature vector is then designed for defect classification to eliminate noise interference. Finally, the effectiveness of the proposed algorithm is experimentally verified during MPCG production process. It is further demonstrated that the algorithm and system developed in this paper could satisfy the requirements of both speed and accuracy under the situation of industrial scene.