Defect detection system for optical element surface based on machine vision

Machine vision is now an excellent tool for testing a wide range of industrial products such as textiles, printed circuit boards, electronic component tags, integrated circuits (ICs) and machine tools. In this paper, we propose an optical lens detection system based on machine vision, which can be widely used in the defect detection of camera lenses, eyeglass lenses and other related optical components. Firstly, the hardware composition of the entire system and the flow of the control system were introduced. Secondly, through the image preprocessing algorithm such as image denoising and contrast enhancement, combined with the algorithm of Canny operator and morphological processing, the reasonable image processing results were obtained. Finally, a decision tree classifier based on C4.5 algorithm was established according to the shape feature of the defect, which realized the automatic recognition and classification of surface defects on optical components. The experimental results show the effectiveness of this method in the detection of optical component defects, which provides a theoretical basis for the functional analysis of optical components in the future. Meanwhile, it can be extended to defect detection in many fields such as circuit boards and integrated circuits.