Liu Jinping, Tang Zhaohui, Xu Pengfei, Liu Wenzhong, Zhang Jin, Zhu Jianyong
College of Mathematics and Computer Science, Hunan Normal University, Changsha 410081, China.
School of Information Science and Engineering, Central South University, Changsha 410083, China.
Sensors (Basel). 2016 Jun 29;16(7):998. doi: 10.3390/s16070998.
The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images' spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines.
由于产品的外观与其内在质量之间存在天然联系，利用智能视觉传感器进行在线产品质量检测（OPQI）这一主题在学术界和工业界都引起了越来越多的关注。从粒状产品（GP）（如谷物产品、织物纺织品）中捕获的视觉图像由大量独立颗粒或随机堆叠的局部均匀碎片组成，对其进行分析和理解仍然具有挑战性。本文提出了一种基于图像统计建模的OPQI方法，用于通过具有半监督学习分类器的威布尔分布（WD）模型对GP质量进行分级和监测。通过全向高斯导数滤波（OGDF）获得的GP图像空间结构的WD模型参数（WD-MPs），在理论上被证明服从积分形式的特定WD模型，并被提取为视觉特征。然后，面对稀缺的标记样本，通过整合两个具有互补性质的独立分类器，利用一种名为COSC-Boosting的协同训练式半监督分类器算法对GP质量进行半监督分级。所提出的OPQI方法的有效性在自动大米质量分级领域与常用方法进行了验证和比较，结果表明该方法具有优越的性能，为GP在装配线上的质量控制奠定了基础。
