Display Omitted A surface defect detection system is proposed for mobile phone screen glass. This system achieves 94% in sensitivity and 97.33% in specificity.The proposed system takes approximate 1.6601s to detect a MPSG. The detection accuracy and speed can meet the needs of online detection for MPSG.Compared with other methods used in the experiment, the proposed improved fuzzy c-means can segment the surface defects in MPSG more accurately. Defect detection using machine vision technology plays an important role in the manufacturing process of mobile phone screen glass (MPSG). This study proposes an improved detection algorithm for MPSG defect recognition and segmentation. Considering the problem of MPSG image misalignment caused by vibrations in the mobile stages, a contour-based registration (CR) method is used to generate the template image used to align the MPSG images. Based on this registration result, the combination of subtraction and projection (CSP) is used to identify defects on the MPSG image, which can eliminate the influence of fluctuation in ambient illumination. To segment the defects with a fuzzy grey boundary from a noisy MPSG image, an improved fuzzy c-means cluster (IFCM) algorithm is developed in this study. A defect detection system is developed, and the proposed algorithms are validated using a number of experimental tests on MPSG images. The testing results demonstrate that the approach proposed in this study can effectively detect various defects on MPSG and that it has better performance than other methods.

Automatic surface defect detection for mobile phone screen glass based on machine vision

Support vector data description (SVDD) has become a very attractive kernel method due to its good results in many novelty detection problems. However, the decision function of SVDD is expressed in terms of the kernel expansion, which results in a run-time complexity linear in the number of support vectors. For applications where fast real-time response is needed, how to speed up the decision function is crucial. This paper aims at dealing with the issue of reducing the testing time complexity of SVDD. A method called fast SVDD (F-SVDD) is proposed. Unlike the traditional methods which all try to compress a kernel expansion into one with fewer terms, the proposed F-SVDD directly finds the preimage of a feature vector, and then uses a simple relationship between this feature vector and the SVDD sphere center to re-express the center with a single vector. The decision function of F-SVDD contains only one kernel term, and thus the decision boundary of F-SVDD is only spherical in the original space. Hence, the run-time complexity of the F-SVDD decision function is no longer linear in the support vectors, but is a constant, no matter how large the training set size is. In this paper, we also propose a novel direct preimage-finding method, which is noniterative and involves no free parameters. The unique preimage can be obtained in real time by the proposed direct method without taking trial-and-error. For demonstration, several real-world data sets and a large-scale data set, the extended MIT face data set, are used in experiments. In addition, a practical industry example regarding liquid crystal display micro-defect inspection is also used to compare the applicability of SVDD and our proposed F-SVDD when faced with mass data input. The results are very encouraging.

Fast Support Vector Data Descriptions for Novelty Detection

We present density weighted support vector data description (DW-SVDD).DW-SVDD improves classification accuracy by introducing density weight into the SVDD.Density weight is relative density of each data points using the k-nearest neighbor (k-NN) approach.DW-SVDD prioritizes data points in high-density regions by applying density weight into the search of description of SVDD.Experimental results demonstrated that the DW-SVDD improved the classification accuracy. One-class classification (OCC) has received a lot of attention because of its usefulness in the absence of statistically-representative non-target data. In this situation, the objective of OCC is to find the optimal description of the target data in order to better identify outlier or non-target data. An example of OCC, support vector data description (SVDD) is widely used for its flexible description boundaries without the need to make assumptions regarding data distribution. By mapping the target dataset into high-dimensional space, SVDD finds the spherical description boundary for the target data. In this process, SVDD considers only the kernel-based distance between each data point and the spherical description, not the density distribution of the data. Therefore, it may happen that data points in high-density regions are not included in the description, decreasing classification performance. To solve this problem, we propose a new SVDD introducing the notion of density weight, which is the relative density of each data point based on the density distribution of the target data using the k-nearest neighbor (k-NN) approach. Incorporating the new weight into the search for an optimal description using SVDD, this new method prioritizes data points in high-density regions, and eventually the optimal description shifts to these regions. We demonstrate the improved performance of the new SVDD by using various datasets from the UCI repository.

Density weighted support vector data description

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Identification of egg’s freshness using NIR and support vector data description

Inline defect inspection plays a critical role in yield improvement for thin film transistor liquid crystal display (TFT-LCD) manufacturing. In array process, some defects are critical to the quality of LCD panels (target defects), while some are not (non-target defects). This paper proposes a target defect identification system by which the target defects can be automatically identified. The proposed system is composed of five parts: projection-based pixel segmentation, normal pixel removal, feature extraction, target defect identification, and decision making. For the identifier design, a novel one-class kernel classifier called fuzzy support vector data description (F-SVDD) ensemble is proposed. F-SVDD ensemble is proposed to solve two critical problems existing in SVDD, including the overfitting due to outliers, and the multi-cluster distribution. In F-SVDD ensemble, both the best number of the F-SVDD members in the ensemble and the elements of each member can be determined by using partitioning-entropy-based kernel fuzzy c-means (KFCM) algorithm. Experimental results, carried out by real defective images provided by a LCD manufacturer, indicate that the proposed F-SVDD ensemble not only greatly improves the performance of SVDD, but also outperforms other commonly used classifiers such as support vector machine (SVM), in terms of target defect identification rate. In addition, the task of target defect identification for one defective image can be accomplished within 3s by the proposed system.

Yi-Ling Hsueh

Papers

Automatic target defect identification for TFT-LCD array process inspection using kernel FCM-based fuzzy SVDD ensemble