Texture features for image classification and retrieval.

The fast and robust automated quality visual inspection has received increasing attention in the product quality control for production efficiency. To effectively detect defects in products, many methods focus on the hand-crafted optical features. However, these methods tend to only work well under specified conditions and have many requirements for the input. So the work in this paper targets on building a deep model to solve this problem. The elaborately designed deep convolutional neural networks (CNN) proposed by us can automatically extract powerful features with less prior knowledge about the images for defect detection, while at the same time is robust to noise. We experimentally evaluate this CNN model on a benchmark dataset and achieve a fast detection result with a high accuracy, surpassing the state-of-the-art methods.

A fast and robust convolutional neural network-based defect detection model in product quality control

Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This article attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs and hot- and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: statistical, spectral, model-based, and machine learning. These works are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

/pdf/automated-visual-defect-detection-for-flat-steel-surface-a-3wjhcxn3aa.pdf

Automated Visual Defect Detection for Flat Steel Surface: A Survey

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.

/pdf/a-review-and-analysis-of-automatic-optical-inspection-and-3pj4dl25sg.pdf

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

Automatic defect detection on the steel surface is a challenging task in computer vision, owing to miscellaneous patterns of the defects, low contrast between the defect and background, the existence of pseudo defects, and so on. In this paper, a new Haar-Weibull-variance (HWV) model is proposed for steel surface defect detection in an unsupervised manner. First, an anisotropic diffusion model is utilized to eliminate the influence of pseudodefects. Second, a new HWV model is established to characterize the texture distribution of each local patch in the image. The proposed model can project the texture distribution of each patch into the low-dimensional space with only two parameters. The parameter distribution of the whole image can also be unified into the form of linear radiation in an Euclidean space. The reliable background can be extracted via the formation of parameter distribution, by which the model parameter can be optimized further. Finally, the adaptive threshold can be determined to distinguish the defect from the background. Experimental results show that the proposed method can detect an arbitrary type of defect on the homogeneously textured surface and achieve an average detection rate of 96.2% on the data set, which outperforms the previous methods.

Steel Surface Defect Detection Using a New Haar–Weibull-Variance Model in Unsupervised Manner

In this paper, we propose a fast regularity measure for defect detection in non-textured and homogeneously textured surfaces, with specific emphasis on ill-defined subtle defects. A small neighborhood window of proper size is first chosen and they slide over the entire inspection image in a pixel-by-pixel basis. The regularity measure for each image patch enclosed in the window is then derived from the eigenvalues of the covariance matrix formed by the variance–covariance of the x- and y-coordinates with the pixel gray levels as the weights for all pixel points in the window. The two eigenvalues of the weighted covariance matrix will be approximately the same when the image patch contains only a homogeneous region, whereas the two eigenvalues will be relatively different if the image patch in the window contains a defect. The smaller eigenvalue of the covariance matrix is then used as the regularity measure. The integral image technique is introduced to the computation of the regularity measure so that it is invariant to the neighborhood window size. The proposed method uses only one single discrimination feature for defect detection. It avoids the use of complicated classifiers in a high-dimensional feature space, and requires no learning process from a set of defective and defect-free training samples. Experimental results on a variety of material surfaces found in industry, including textured images of plastic surfaces and leather and non-textured images of backside solar wafers and LCD backlight panels, have shown the effectiveness of the proposed regularity measure for surface defect detection. It is computationally very fast, and takes only 0.032 s for a 400 × 400 image on a Pentium 3.00 GHz personal computer. In a test set of 73 backside solar wafer images involving 53 defect-free and 20 defective samples, the proposed regularity measure can correctly identify all the test images.

A fast regularity measure for surface defect detection

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A fast regularity measure for surface defect detection