引用本文:戴铭,叶木超,周智恒,等.基于先验分布活动轮廓模型的纹理缺陷检测[J].控制与决策,2020,35(5):1226-1230
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基于先验分布活动轮廓模型的纹理缺陷检测
戴铭1, 叶木超1, 周智恒1, 杨志伟2
(1. 华南理工大学电子与信息学院,广州510640;2. 广州智慧城市研究院,广州510060)
摘要:
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.
关键词:  缺陷检测  纹理背景  活动轮廓模型  先验分布
DOI:10.13195/j.kzyjc.2018.1220
分类号:TP391
基金项目:国家自然科学基金项目(61871188);国家重点研发计划项目(2018YFC0309400).
Texture defects detection based on prior distribution active contour model
DAI Ming1,YE Mu-chao1,ZHOU Zhi-heng1,YANG Zhi-wei2
(1. School of Eectronics and Information Engineering,South China University of Technology,Guangzhou510640,China;2. Guangzhou Smart City Research Institute,Guangzhou510060,China)
Abstract:
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.
Key words:  defects detection  textures background  active contour model  prior distribution

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