A Surface Defect Detection Framework for Glass Bottle Bottom Using Visual Attention Model and Wavelet Transform

Zhou, Xianen; Wang, Yaonan; Qing, Zhu; Mao, Jianxu; Xiao, Changyan; Lu, Xiao; Zhang, Hui

doi:10.1109/tii.2019.2935153

Cited by 99 publications

(43 citation statements)

References 34 publications

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“…The commonly used methods for circle fitting include the centroid method, 15 Hough transform, 16 and least squares method. 17 In this study, the least squares method is used to fit the circle and find its center, as it is fast and accurate. 18 The fitted radius of the oil seal is R , that is, r 3 in Figure 4.…”

Section: Multidetection Region Segmentation Methods Based On Superpixel Segmentationmentioning

confidence: 99%

Oil seal surface defect detection using superpixel segmentation and circumferential difference

Liu

Jiang

et al. 2020

International Journal of Advanced Robotic Systems

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Surface defects affect the quality and safety of oil seals. It is a challenge to detect such defects in a vision system because of the unequal reflection property of oil seal surfaces and low contrast between the defect and the background. This article proposes a visual detection method (VDM) for oil seal surface defects and outlines two key issues of VDMs. First, we present a superpixel segmentation algorithm based on the significant gray level variation in the radial direction of an oil seal surface image. This image is then divided into several ring belts. Subsequently, considering the reflection inequality and low contrast, we propose a new circumferential background difference algorithm based on the small variation along the circumferential direction of the image. This algorithm eliminates the influence of the reflection inequality and improves the contrast distinction between the defects and the background. The experimental results verify the effectiveness of the proposed method with a recall and precision as high as 95.2% and 86.8%, respectively.

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Section: Multidetection Region Segmentation Methods Based On Superpixel Segmentationmentioning

confidence: 99%

Oil seal surface defect detection using superpixel segmentation and circumferential difference

Liu

Jiang

et al. 2020

International Journal of Advanced Robotic Systems

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“…In recent years, machine vision-based approaches have received immense attention. They are widely applied to the tasks of object detection, object tracking, virtual measurement and so forth [41,42]. They can further be used for online inspection (e.g.…”

Section: Key Technologies Boosting Digital Twin Realizationmentioning

confidence: 99%

Industrial applications of digital twins

Jiang

Yin

et al. 2021

Phil. Trans. R. Soc. A.

174

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A digital twin (DT) is classically defined as the virtual replica of a real-world product, system, being, communities, even cities that are continuously updated with data from its physical counterpart, as well as its environment. It bridges the virtual cyberspace with the physical entities and, as such, is considered to be the pillar of Industry 4.0 and the innovation backbone of the future. A DT is created and used throughout the whole life cycle of the entity it replicates, from cradle to grave, so to speak. This article focuses on the present state of the art of DTs, concentrating on the use of DTs in industry in the context of smart manufacturing, especially from the point of view of plantwide optimization. The main capabilities of DTs (mirroring, shadowing and threading) are discussed in this context. The article concludes with a perspective on the future. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

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“…The traditional manual visual inspection methods with the disadvantages of low detection rate, poor real-time performance, low detection confidence, and poor environmental adaptability are not sufficient for the requirements of high precision and fast speed of industrial surface defect detection. With the rapid development of machine vision technology, the automated surface defect detection methods based on machine vision have gradually become the mainstream methods and have been widely used in the surface defect detection of glass bottles [ 12 ], mobile phone screens [ 13 ], automobile carbon brushes [ 14 ], polysilicon solar cells [ 15 ], especially steel plates and strips [ 9 ], aluminum plates and aluminum strips [ 16 ], copper plates and copper strips [ 17 ], and other metal planar materials. A machine vision inspection system usually includes an image acquisition subsystem and region of interest detection (ROI) subsystem.…”

Section: Two-dimensional Surface Quality Inspection Systemmentioning

confidence: 99%

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Fang

Luo

Zhou

et al. 2020

Sensors

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The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.

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A Surface Defect Detection Framework for Glass Bottle Bottom Using Visual Attention Model and Wavelet Transform

Cited by 99 publications

References 34 publications

Oil seal surface defect detection using superpixel segmentation and circumferential difference

Oil seal surface defect detection using superpixel segmentation and circumferential difference

Industrial applications of digital twins

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

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