Adaptive intelligent detection technology for digital products' shell surface

As different types of digital products have different superficial optical characteristics, a visual detection method adaptive to various surface types is proposed to improve the reliability of defect detection. Firstly, after the image collection under different light sources, materials are classified according to the recognition results of gray statistic analysis. Secondly, 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. Experimental results show that the proposed algorithm is highly robust and resistive to external disturbances. Moreover, comprehensive performance analysis indicates that the proposed algorithm produces a false alarm rate lower than 5% and an accuracy rate higher than 93%. Besides, the high detection speed makes the algorithm possible to be applied to actual production. ©, 2015, South China University of Technology. All right reserved.