Research On The Algorithm And Implementation Of A Glass Quality On-line Detection System

With the rapid development of society and economy, the quality requirement of glass is higher and higher, to increase the automotive level of China glass industry, and aiming at the serious lag of theories study and system development to online quality detection of glass, real-time processing algorithm and theory of motion image that based on machine vision technology is studied in the thesis, and some key technologies of glass quality online detection system are realized.Total technology scheme of online quality detection system of glass is proposed according the analyses to the technical requirement and application feature of glass detection. The principle of glass defect recognition is explained. The aim of system and technical requirement is proposed. The system of detection is designed from two points of hardware and software. The system of hardware is divided into five modules. The total design frame of software is proposed. The flow of image processing and analyze is expounded as the emphasis.An acquisition system of glass image is designed. The images are compared which are acquired with different lamp-houses. The grab procedure is realized using the library of grab-board. The flow of preprocess is designed, including linear transform, median filter and the eliminating of eliminate motion blur. The flow results in strengthened gray image of glass. We distill the template of stand image to reduce the influence of environments. The real-time image is subtracted from stand template to get the image of detection.The image needs to be divided into several areas. Each area choose different threshold. A self-adapted algorithm of threshold selection is proposed. The boundary of target could be distilled by the algorithm of edge detection based on differential coefficient operator. Based on the feature of sample defect, a new method of local threshold-division is adopted to get the threshold of area. The noise could be eliminated using basic algorithm that base on the mathematic morphology. The lookup and orientation of defect adopt RLE algorithm. We design the flow of dust-elimination according to the rhythm of glass image. Several features are abstracted to distinguish the types of defect.Neural network is designed as sorter. On the base of analyzing standard BP algorithm, in allusion to the limitations that standard BP algorithm have, some improved methods are given. Two different training methods are compared. The improved algorithm is realized in Visual C++ environment. Experiments are done in allusion to pattern classify and character recognize. The results show that this BP algorithm is more efficient on the aspects of convergence rate and the precision of recognize.At last, the online quality detection system of glass is realized, including hardware and software. Now the system is running on the field.