Abstract
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.
Highlights
Product quality is the driving force for every enterprise, which is an important factor to keep an impregnable position in the modern global competitive environment [1,2]
The larger the amount of training samples available for the supervised learning classifier, the speaking, the larger the amount of training samples available for the supervised learning classifier, the better the generalization or performance that can be achieved in a practical application
Where if and only if both mean square error (MSE) ph thin plate spline regression classifier (TPSRC) q and MSE ph multivariate adaptive regression spline classifier (MARSC) q satisfy the above conditions at the same time, the unlabeled sample is confirmed as a high confidence labeled sample candidate and can possibly be used to refine the classifier, in other words, we accept hTPSRC pxq and hMARSC pxq at the condition when MSE declines and the labeled set is augmented by pxu, ŷu q who maximizes the values of MSE phl q
Summary
Product quality is the driving force for every enterprise, which is an important factor to keep an impregnable position in the modern global competitive environment [1,2]. Processing communities for industrial manufacturing, safety production monitoring, quality processes, owing to the intrinsic merits of visual inspection technologies, such as fast response, high. Visual sensors-based OPQI is essential and indispensable in most product processing efficiency, non-intrusiveness, economy, flexibility and so on, onasassembly production processes, owing to the intrinsic merits of visual inspection technologies, such fast response, high efficiency, economy, and socoupled on, on assembly production lines. Recentnon-intrusiveness, advances in visual sensorflexibility technologies with image processing and analysis. Though the GP image (GPI) is an effective and a direct indicator of the inner quality of the and so on. Though the GP image (GPI) is an effective and a direct indicator of the inner quality of the corresponding GP, GPI processing and analysis is not a simple matter. Results from theoperator, canny operator, Sobel operator are post-processed byOtsu’s using Otsu’s threshold
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