A surface defects inspection method based on multidirectional gray-level fluctuation

Abstract

Machine vision inspection technology provides an efficient tool for surface defects inspection. However, because of the multiformity of surface defects, the existing machine vision methods for surface defects inspection are limited by application scenarios. In order to improve the versatility of algorithms, and to process various kinds of images more accurately, we propose a new adaptive method for surface defect detection, named neighborhood gray-level difference method using the multidirectional gray-level fluctuation. This method changes thresholds and step values by extracting gray-level-fluctuating condition of images, and then it uses the neighborhood gray-level difference to segment defects from background. Experimental results demonstrate the effectiveness of the proposed method for inspecting different surface defects. Compared with other methods, the proposed method can be applied to inspect various surface defects, and it can provide more accurate defect segmentation results.