Large-Complex-Surface Defect Detection by Hybrid Gradient Threshold Segmentation and Image Registration

Machine vision-based object detection techniques have been widely used in product inspection, defect detection, and dimension measurement. These techniques have largely improved the efficiency of industrial production and increased the level of production autonomy. However, demands on advance hardware design and image processing algorithms are needed for the quality inspection of a large-complex-surface. In order to solve this problem, a hybrid surface defect detection method is developed. An image of the product surface is first divided into two areas: background with similar features and special pattern area, such as product trademarks. For the background area, defects have significant differences in gray intensity from the normal area. Fault detection is conducted using a gradient threshold segmentation method that can limit segmentation errors arising from uneven illuminations. For the special pattern area, image registration and image difference are adopted to detect defects, which are adaptive to irregular image contents with discontinuous shapes and appearances. Experimental results indicate that the proposed method achieves about 1.21 times and 2.94 times higher accuracy, in F-measure, for large-complex-surface defect detection than the traditional methods of gradient threshold segmentation and template matching, respectively. The proposed image processing technique can be applied in product quality inspections.