Intelligent defect classification system based on deep learning

Ye, Ruifang; Pan, Chia-Sheng; Chang, Ming; Yu, Qin

doi:10.1177/1687814018766682

Cited by 33 publications

(12 citation statements)

References 14 publications

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“…Many works applied machine learning for inspection types of tasks, such as surface defect inspection, 12 AVI of machine components, 13 and AVI of microdrill bits in printed circuit board production. 14 With the rise of the interest in deep learning, this specific technology was also applied to a wide range of AVI tasks, for example, structural inspection, 15 surface defect detection and recognition, 16 defect classification system, 17 and others. [18][19][20][21] Even design tools for the creation of deep convolutional networks for AVI were developed.…”

Section: Related Workmentioning

confidence: 99%

Classification using a three-dimensional sensor in a structured industrial environment

Mikhailov

Jovančević²,

Mokhtari³

2020

J. Electron. Imag.

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Usage of a three-dimensional (3-D) sensor and point clouds provides various benefits over the usage of a traditional camera for industrial inspection. We focus on the development of a classification solution for industrial inspection purposes using point clouds as an input. The developed approach employs deep learning to classify point clouds, acquired via a 3-D sensor, the final goal being to verify the presence of certain industrial elements in the scene. We possess the computer-aided design model of the whole mechanical assembly and an in-house developed localization module provides initial pose estimation from which 3-D point clouds of the elements are inferred. The accuracy of this approach is proved to be acceptable for industrial usage. Robustness of the classification module in relation to the accuracy of the localization algorithm is also estimated. Fig. 1 (a) Robot-based inspection system with three cameras mounted on an end effector and (b) Ensenso N35 3-D sensor.

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Section: Related Workmentioning

confidence: 99%

Classification using a three-dimensional sensor in a structured industrial environment

Mikhailov

Jovančević²,

Mokhtari³

2020

J. Electron. Imag.

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“…The Gaussian kernel takes only one parameter that the kernel radius σ . In experiments, the penalty parameter ζ is also treated as a nuclear parameter and is included in the uniform frame with the nuclear radius [23], [24].…”

Section: Hinge Loss Functionmentioning

confidence: 99%

Research and Application of Element Logging Intelligent Identification Model Based on Data Mining

2019

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Underground strata are reflected in various information sources in petroleum exploration, including good logging and drilling data. Real-time measurement parameters obtained from mud logging can provide data support for the early discovery of oil and gas resources and the prevention of safety accidents. It plays a forward-looking role in the drilling process. In this paper, we aim at the defection of fuzzy and random characteristics of the big data of drilling element parameters in the current drilling process. A new method named grey wolf optimization-support vector machine (GWO-SVM) is proposed by analyzing the relationship between logging data and formation to solve the serious problem of formation misjudgment. Using element content and Gamma-ray value, data mining is performed by a large number of real-time data obtained from the drilling site. The obtained information is used for comprehensive estimation and prediction of strata. First, the data is normalized, and then, the best ζ and σ values are found through the optimization of gray wolf algorithm, next the SVM training is carried out, finally, the formation prediction model is established, and the error analysis of the results was conducted. In the paper, the algorithm model is subsequently applied to three actual wells. The GWO-SVM model based on drilling data is used to predict the formation, and the error analysis showed that the error range of the GWO-SVM algorithm is within 10%. Compared with the GWO-SVM, the model accuracy of SVM, Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithm is lower 53% and 23%, respectively. The GWO-SVM has higher robustness, reliability, and achieves faster convergence speed, stronger generalization effect, and improves the identification accuracy of elements for the formation. The average accuracy of the GWO-SVM in stratum dynamic identification is 93.5%. This model is implemented to support the logging system to improve application strength.INDEX TERMS Data mining, element logging, error analysis, gray wolf algorithm, support vector machine. I. INTRODUCTIONData mining includes 8 processes, such as data cleaning, data transformation, data mining process, pattern evaluation, and knowledge representation [1]. Data mining can acquire useful analytical information by the selection of appropriate analytical tools, using of statistical methods, rule reasoning, fuzzy sets, genetic algorithms and other methods to handle information. The four main data mining tasks include modeling prediction, association analysis, clustering analysis, anomaly detection. Among the classification methods used to predict discrete target variables, the nature-inspired optimizationThe associate editor coordinating the review of this manuscript and approving it for publication was Patrick Hung.

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“…Thus, removing defective beans becomes a necessary step before brewing for significantly increasing their competition and profits [2]. The SCAA has classified the defective beans into 13 classes [3], as shown in upper portion of Figure 1. Most popular defective bean removal processes are achieved by manual or mechanical manners in the past decades [1,2,4,5].…”

Section: Introductionmentioning

confidence: 99%

“…They considered certain properties (color, morphology, texture, and a combination of morphology and color features), which cover only some of defective beans. For providing a uniform solution to industries, computational intelligent technologies, such as pattern mining [13][14][15] or machine/deep learning [3,[16][17][18][19], bring considerable techniques to develop actionable analytics and prediction to completely detect all sorts of detective bean patterns at the same time. Deep-learning-based models are inherently more suitable for accomplishing complex tasks with enormous data inputs than generic data-analytic-based models.…”

Section: Introductionmentioning

confidence: 99%

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Deep-Learning-Based Defective Bean Inspection with GAN-Structured Automated Labeled Data Augmentation in Coffee Industry

et al. 2019

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In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to 80 % .

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Intelligent defect classification system based on deep learning

Cited by 33 publications

References 14 publications

Classification using a three-dimensional sensor in a structured industrial environment

Classification using a three-dimensional sensor in a structured industrial environment

Research and Application of Element Logging Intelligent Identification Model Based on Data Mining

Deep-Learning-Based Defective Bean Inspection with GAN-Structured Automated Labeled Data Augmentation in Coffee Industry

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