Research On Adaptive And Intelligent Visual Detection Technology For Digital Product Cover Surface Defects

Modern industrial manufacturing has entered a period of rapid development. The consumers enjoy their lives with rich and colorful user experience a variety of electronic products has brought, while the manufacturing is required strictly. In order to improve the efficiency and cut the cost of the traditional artificial quality inspection, the machine vision detection technology finally emerges as the times require. Instead of the artificial vision, the technology which is based on the camera and computer systems, will locate and recognize the defects by automatic detection and intelligent judgment.The adaptive intelligent visual detection method based on machine vision is used for shell surface defects of digital products.To improve the speed, precision and intelligent level for the quality detection of mobile phone shell, a series of the key technologies are researched and designed. Firstly, the effects of illumination and the lighting design reasonably, with the selection of the camera and lens on imaging quality are discussed. The main work is based on the imaging model, the modulation transfer function, and the important mechanism of their parameters. Moreover,in the detecting, the region of interest is positioned with the Intelligent Positioning Algorithm, some appropriate features are selected according to the gray statistical analysis results, then the GRNN neural network classifier is used to classify the materials with these features. A hybrid threshold segmentation algorithm, which is on the basis of global and dynamic threshold segmentation techniques, as well as an improved curve detector, which uses Gaussian filter and partial derivative feature to find out the curve’s key points and then connects the key points into a line through the “relaxation” algorithm, is used to detect different given surfaces. Finally, a small part of the mobile phone shell contour matching algorithm is proposed to detect the micro size.By debugging the system,it verify the feasibility of the software and hardware solutions.Experimental results show that algorithms perform with the strong robustness and high precision. Besides, the high detection speed makes the algorithm possible to be applied to actual production.