Tyre Defect Detection Based on GLCM and Gabor Filter

Shabir, Muhammad; Hassan, Muhammad Umair; Yu, Xiangru; Li, Jinping

doi:10.1109/inmic48123.2019.9022777

Cited by 10 publications

(7 citation statements)

References 11 publications

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“…Statistical approaches assess the geographic distribution of pixel values by extracting statistical information from defect images. Histogram information [6][7][8][9][10][11], co-occurrence matrices [12][13][14][15][16][17][18], and local binary patterns (LBP) [19][20][21][22][23][24] have all been presented as statistical methods for defect detection. Statistical approaches can present the anomalies in an intuitive and discriminative manner, and they are simple to model, interpret, and display.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Cui¹,

Liu²,

Lian³

2022

Preprint

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In line with the development of Industry 4.0, more and more attention is attracted to the field of surface defect detection. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in industry field, where deep learning-based algorithms performs better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and a significant amount of labor, but it is also inefficient and has certain limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling above disadvantages for visual anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, information on publicly available datasets containing surface image samples are provided. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.

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Section: Related Workmentioning

confidence: 99%

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Cui¹,

Liu²,

Lian³

2022

Preprint

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“…X.J. Jia proposed an edge detection method based on OpenCV, an open source computer vision library, but detecting multiple flaws is difficult [25]. M.A.…”

Section: Related Workmentioning

confidence: 99%

A Two-model Automatic Towel Defect Detection Method Based On YOLOv5

Ding¹,

Zhou²,

Du³

et al. 2022

Proceedings of International Exchange and Innovation Conference on Engineering &Amp; Sciences (IEICES)

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For replacing error-prone, costly, incongruous, and time-consuming human operators in the textile industry, an automatic and precise fabric flaw inspection system is vital. Textile blemish detection plays an essential effect in the production of textiles in the textile industry. Researchers have proposed many algorithms for fabric defect detection. However, several important issues, including inference time consumption, data imbalance, and algorithm complexity, still need to be dealt with for application in textile industry production. This paper proposes a two-model automatic towel defect detection method based on YOLOv5 to detect the defects of towels, effectively improving the detection efficiency and significantly reducing the cost. Moreover, this method has achieved the detection speed of 10 pictures per second in the actual production.

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“…Haralick et al reported that the GLCM was a method to quantify the spatial relationship between adjacent pixels in an image [17]. GLCM is widely used in disease detection [18,19], skin texture analysis [20], and defect detection [21], etc. Given the above, GLCM is feasible and efficient in image texture feature analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Haralick et al report that the GLCM is a method to quantify the spatial relationship between adjacent pixels in an image [23]. GLCM is widely used in disease detection [24]- [26], skin texture analysis [27], defect detection [28], fabric classification [29], egg fertility identification [30], etc. Safira et.al report a research to detect abnormalities of nails.…”

mentioning

confidence: 99%

Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone

Chen

Hua

et al. 2021

IEEE Sensors J.

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Coronavirus Disease 2019 (COVID-19) has spread all over the world since it broke out massively in December 2019, which has caused a large loss to the whole world. Both the confirmed cases and death cases have reached a relatively frightening number. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of COVID-19, can be transmitted by small respiratory droplets. To curb its spread at the source, wearing masks is a convenient and effective measure. In most cases, people use face masks in a high-frequent but short-time way. Aimed at solving the problem that we do not know which service stage of the mask belongs to, we propose a detection system based on the mobile phone. We first extract four features from the gray level co-occurrence matrixes (GLCMs) of the face mask's micro-photos. Next, a three-result detection system is accomplished by using K Nearest Neighbor (KNN) algorithm. The results of validation experiments show that our system can reach an accuracy of 82.87% (measured by macro-measures) on the testing dataset. The precision of Type I 'normal use' and the recall of type III 'not recommended' reach 92.00% and 92.59%. In future work, we plan to expand the detection objects to more mask types. This work demonstrates that the proposed mobile microscope system can be used as an assistant for face mask being used, which may play a positive role in fighting against COVID-19. Index Terms-COVID-19 pandemic, use time of face mask, textural feature, SARS-CoV-2, machine learning, image processing. I. INTRODUCTIONC OVID-19 has wreaked havoc around the world and the spread of COVID-19 has not been curbed totally by June, 2020. According to the situation report of the World Health Manuscript

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Tyre Defect Detection Based on GLCM and Gabor Filter

Cited by 10 publications

References 11 publications

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

A Two-model Automatic Towel Defect Detection Method Based On YOLOv5

Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone

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