Clear Off Bolt Inf 7 Letters

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Clear Off Bolt Inf 7 Letters

Clear Off Bolt Inf 7 Letters

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By Yajun Chen 1, 2, * , Yuanyuan Ding 1 , Fan Zhao 1, 2 , Erhu Zhang 1, 2 , Zhangnan Wu 1 and Linhao Shao 1

Received: 21 June 2021 / Revised: 12 August 2021 / Accepted: 17 August 2021 / Published: 20 August 2021

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The complete intellectual development of the manufacturing industry puts forward new requirements for monitoring the quality of industrial products. This paper summarizes the current research status of machine learning methods in the detection of surface defects, a key component in industrial product quality monitoring. First of all, according to the use of surface features, the application of conventional machine learning in the detection of defects in product technology is summarized from three aspects: visual features, color features , and features. Second, the research status of detecting the defects of business products based on deep learning technology in recent years will be discussed from three aspects: the observation method, the method in neglected, and the weakened condition preserved. Then, the most common critical problems and their solutions in identifying business failures are summarized; Important problems include real-time problem, small-scale problem, small-scale objective problem, nonlinear simulation problem. Finally, the most common data used for industrial surface defects in recent years have been compiled, and the latest research methods have been compared on the MVTec AD dataset, in order to provide a direction for new research and development in skin defect detection technology.

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Industrial products; lack of knowledge; deep learning; unbalanced signals; image dataset industrial products; lack of knowledge; deep learning; unbalanced signals; image file

In the industrial production process, due to the shortcomings and limitations of the current technology, working conditions, etc., the quality of the manufactured products is more flexible. Among them, global defects are the most intuitive evidence of product quality. Therefore, in order to ensure the educational ratio and reliable quality, it is necessary to identify the defects of the product [1, 2]. “Defect” can generally be understood as an absence, flaw or defect compared to the standard sample. The comparison between the normal sample and the defect sample of the industrial products is shown in Figure 1. The detection of surface defects involves the detection of scratches, defects, protection foreign matter, color contamination, holes, and other defects on the surface of the sample i. to be tested, to obtain relevant information such as the type, contour, location, and amount of surface defects of the sample to be tested [3]. Detecting defects by hand is the most common practice, but this method is cheap; It cannot meet the demands of real-time information. It was gradually replaced by other methods.

Clear Off Bolt Inf 7 Letters

Now, some experts have started the practical research on surface defect detection, based on the latest methods, applications, critical problems, and many other aspects [ 4]. Document [5] summarizes the current research status of defect detection techniques such as magnetic particle inspection, penetrant inspection, current inspection, ultrasonic inspection, machine learning, and deep learning [6, 7]; compare and contrast the advantages and disadvantages of the above methods; and combines defect detection technology in electrical components, pipes, welding parts, mechanical parts, and general applications in quality control. From supervised learning model, unsupervised learning model [8], and other methods [9] (semi-supervised learning model and weak learning model), literature [ 10] analyze the defect detection methods based on deep learning, and then, three important problems of real time, small samples, and comparison with traditional images based on the nature of the detection of defects in the surface of the skin. After looking at the visual inspection (AOI) technique, the document [11] described some steps and related methods used in the technique for defect detection. Document [12] is the first to list the differences in the field of defects; Major techniques and deep learning methods used for disability detection are introduced and compared. After that, the applications of ultrasonic detection and deep learning methods in defect detection are explored. Finally, the existing applications were investigated and based on defect detection equipment, some challenges were proposed for defect detection, such as three-dimensional vision, high precision, high quality, fast detection, small boxes, etc. known in the field of defect detection of industrial products, there is currently little literature review on machine learning methods, and although some books summarize the issues and problems in identifying surface defects for industrial products, their solutions and instructions are insufficient. In addition, in terms of data, there is no complete set of data on skin defect information of industrial products. Therefore, in order to solve the above problems, this paper will firstly summarize the research status of product technology defect detection from the traditional machine learning method and deep learning method, in after that, the main problems in the process of seeing the surface of the technology, really. -Time problems, small scale problems, small target problem, unbalanced signal problem, are discussed, and some solutions are given for each problem. Finally, the industry’s failure detection data were compiled, and the new methods were compared with the MVTec AD.

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Figure 1. Normal samples (column 1) and defective samples (columns 2-6). The defective parts are shown below the picture. Examples taken from the MVTec AD database [13]: first row: skin; second row: tile; the third row: phone. (MVTec AD is a database proposed by MVTec Software GmbH in 2019 for comparing various types of Anomaly Detection, focusing on technical information and AD is the abbreviation of Anomaly Detection.).

The publication time of the references in this review is the most important after 2016 because these documents can show the development of the latest technology. By looking at the relevant reviews, it was decided how to organize this paper according to traditional methods, latest methods, important problems, and tools (datasets) ie the nature of this paper. The main points of this paper are as follows: Part 2, a summary of product surface defect detection methods based on conventional machine vision algorithm; Part 3, a summary of defect detection methods on industrial products based on deep learning; Part 4, key issues and their solutions and discussion; Section 5, collection and analysis of industrial product surface defect detection data and comparison of the latest methods of MVTec AD database.

Traditional methods of skin tanning have been around for quite some time. This chapter separates the method of detecting defects in the surface of the technology based on machine vision from high resolution image. Based on the different characteristics, they are mainly divided into three categories: the type of appearance, the type of color appearance, the type of appearance. The detailed chapter layout is shown in Figure 2 .

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The feature shows the homogeneity in the image and can show the configuration and the configuration properties of the surface of the image through the gray distribution of the pixels and their neighboring areas. Model-based methods can be divided into four categories: statistical method, symbolic method, functional method, and modeling method [14, 15].

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For the statistical method, the main idea is to treat the gray value distribution on the surface of an object as a uniform distribution, analyzing the distribution of variables from the point of view of statistics. , and describes the point distribution of the gray value through the histogram feature. gray level matrix, local binary principle, autocorrelation function, morphological mathematics, and others.

For signal processing, the main idea is to store the image as a two-dimensional signal and to analyze the image from the point of view of the signal filter.

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