Automated inspection of engineering ceramic grinding surface damage based on image recognition

Chen, Shangong; Lin, Bin; Han, Xuesong; Liang, Xiaohu

doi:10.1007/s00170-012-4338-2

Cited by 40 publications

(20 citation statements)

References 28 publications

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“…After loading the image, the foreground and background are separated using the K-means algorithm to eliminate parts of the image that do not contain cracks. Many issues arise because of shadows, so a median filter is applied to the image, and the outcome is subtracted from the original image [28].…”

Section: Crack Identificationmentioning

confidence: 99%

“…A percolation approach has also been used successfully to identify cracks by eliminating background pixels and assuming all foreground darker pixels are cracks [27]. Periodic image noise removal has been used for crack identification [28]. Edge detection has been implemented for many purposes, but it has large potential for crack identification [29].…”

Section: Introductionmentioning

confidence: 99%

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Bridge related damage quantification using unmanned aerial vehicle imagery

Ellenberg

Kontsos

Moon

et al. 2016

Struct. Control Health Monit.

135

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Visual inspection procedures remain the primary method of infrastructure assessment throughout the USA, but their shortcomings are numerous. In addition to their widely acknowledged variability and subjectivity, the large scale of civil infrastructure systems presents expensive access and time requirements that constrain the frequency of visual inspections and result in poor temporal resolution, which hampers effective decision-making. To overcome this challenge, the research reported herein aimed to assess the ability of computer algorithms together with imagery collected by unmanned aerial vehicles (UAV) to extract accurate and quantitative information to help inform infrastructure management decisions. Techniques such as homography and lens distortion correction are used in this article in a post-processing framework that allows the use of color images obtained by UAVs for actual damage quantification measurements. The experiments described in this article utilize a UAV with a mounted camera and provide measurements from a representative infrastructure mockup with several simulated damage scenarios. Deformation measurements, change detection (related to structural features and the size of deterioration), and crack pattern identification were all analyzed. The results indicated that the developed post-processing algorithms were able to extract quantitative information from UAV captured imagery.

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Section: Crack Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bridge related damage quantification using unmanned aerial vehicle imagery

Ellenberg

Kontsos

Moon

et al. 2016

Struct. Control Health Monit.

135

View full text Add to dashboard Cite

show abstract

“…Moreover, there is a study which investigates neural networks with the pyramid wavelet transform (Wong et al, 2009), as well as statistical analysis (Lin, 2009). An automatic damage detection system for engineering ceramic surfaces with image processing techniques, pattern recognition and machine vision is presented in Chen et al (2013).…”

Section: Introductionmentioning

confidence: 99%

The Use of Haar Wavelets in Detecting and Localizing Texture Defects

Vaidelienė

Valantinas

2016

Image Anal Stereol

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In this paper, a new Haar wavelet-based approach to the detection and localization of defects in grey-level texture images is presented. This new approach explores space localization properties of the discrete Haar wavelet transform (HT) and generates statistically-based parameterized texture defect detection criteria. The criteria provide the user with a possibility to control the percentage of both the actually defect-free images detected as defective and/or the actually defective images detected as defect-free, in the class of texture images under investigation. The experiment analyses samples of ceramic tiles, glass samples, as well as fabric scraps, taken from real factory environment.

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“…Detection of defects in texture surfaces, such as steel plates, weldment, ceramic tiles, fabric, etc., is an important area of automated industrial inspection systems. Numerous methods and approaches have been proposed for performing this task [1][2][3][4][5]. With reference to many texture analysis survey papers, the texture image analysis techniques, used for visual defect inspection, can be categorized as follows: statistical approach, structural approach, filter-based approach and the model-based approach.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Discrete Wavelets in Implementing Defect Detection System for

Vaidelienė

Valantinas

Ražanskas³

2016

ITC

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In this paper, a novel wavelet-based approach to the detection of defects in grey-level texture images is proposed. This new approach (system) explores specific properties of the discrete wavelet transform (DWT), evaluates the statistical analysis results associated with well-defined and task-oriented subsets of DWT spectral coefficients, and generates defect detection criteria which, in their turn, evaluate many-sided nature of potential defects in texture images and leave space for controlling the risk, i.e. for controlling the percentage of false positives and/or false negatives in a particular class of texture images. The experimental results demonstrating the use of the proposed system for the visual inspection of ceramic tiles, obtained from the real factory environment, and textile fabric scraps are also presented.

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Automated inspection of engineering ceramic grinding surface damage based on image recognition

Cited by 40 publications

References 28 publications

Bridge related damage quantification using unmanned aerial vehicle imagery

Bridge related damage quantification using unmanned aerial vehicle imagery

The Use of Haar Wavelets in Detecting and Localizing Texture Defects

On the Use of Discrete Wavelets in Implementing Defect Detection System for

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