A novel algorithm for defect extraction and classification of mobile phone screen based on machine vision

Li, Changsheng; Zhang, Xianmin; Huang, Yanjiang; Tang, Cheng; Fatikow, Sergej

doi:10.1016/j.cie.2020.106530

Cited by 44 publications

(19 citation statements)

References 23 publications

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“…Melnyk and Tushnytskyy [249] proposed a PCB defect detection and classification system that implemented the K-means clustering algorithm. Li et al [250] proposed a clustering algorithm that links the regions that are close to each other to detect cluster defects composed of many small point defects. The schematic diagram of the process of connecting domains A and B in the clustering method is shown in Fig.…”

Section: Characteristicsmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

356

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Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

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Section: Characteristicsmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

356

View full text Add to dashboard Cite

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“…( g ) Lack defect of gear [ 24 ]. ( h ) light leakage defect on mobile screen [ 25 ]. ( i ) Convexity defect in aluminum foil [ 26 ].…”

Section: Figurementioning

confidence: 99%

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

et al. 2020

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The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.

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“…Bo et al [5] detected the surface defects of tiles. Li et al [6] proposed a novel defect extraction and classification scheme for mobile phone screen based on machine vision. Zhu et al [7] presented a machine vision based method for detecting surface defects of pipe joints.…”

Section: Introductionmentioning

confidence: 99%

A Method of Surface Defect Detection of Irregular Industrial Products Based on Machine Vision

Jia²,

Lü³

et al. 2021

Wireless Communications and Mobile Computing

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In recent years, the surface defect detection technology of irregular industrial products based on machine vision has been widely used in various industrial scenarios. This paper takes Bluetooth headsets as an example, proposes a Bluetooth headset surface defect detection algorithm based on machine vision to quickly and accurately detect defects on the headset surface. After analyzing the surface characteristics and defect types of Bluetooth headsets, we proposed a surface scratch detection algorithm and a surface glue-overflowed detection algorithm. The result of the experiment shows that the detection algorithm can detect the surface defect of Bluetooth headsets fast as well as effectively, and the accuracy of defect recognition reaches 98%. The experiment verifies the correctness of the theory analysis and detection algorithm; therefore, the detection algorithm can be used in the recognition and detection of surface defect of Bluetooth headsets.

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A novel algorithm for defect extraction and classification of mobile phone screen based on machine vision

Cited by 44 publications

References 23 publications

State of the Art in Defect Detection Based on Machine Vision

State of the Art in Defect Detection Based on Machine Vision

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

A Method of Surface Defect Detection of Irregular Industrial Products Based on Machine Vision

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