基于先验分布活动轮廓模型的纹理缺陷检测

在工业产品的表面缺陷检测中,计算机视觉逐渐取代人工视觉,这是工业自动化的重要标志之一.而产品的表面纹理对缺陷检测的干扰一直是个难点.从图像分割的角度出发,以缺陷为目标,将纹理表面作为背景提取产品的表面缺陷.基于非参数统计活动轮廓模型提出一种先验分布模型,即以纹理的灰度分布作为背景的先验信息,使得算法更容易区分纹理背景和缺陷.实验结果表明,所提出的算法适用于不同纹理背景的缺陷检测,能准确地提取缺陷位置.

Texture defects detection based on prior distribution active contour model

Human vision is gradually substituted by computer vision in the field of products surface detection. This is the inevitable trend of industrial automation control. However, surface texture is always a difficult point for defects detection. This paper starts from the angle of image segmentation, treating defects as objects and textures as backgrounds, to extract the surface defects. Based on the non-parametric statistical activie contour model, a distribution prior model is proposed. Our model utilizes the distribution of textures as the prior distribution of the background, which makes it easier to distinguish the defects and the textures. Experiment results show that the proposed method adapts to defects detection in different texture background, and exactly extracts the location of defects.