Application of two dimensional wavelet for defect detection in steel process

Sadeghi, Mostafa; saraie, Shokoufeh Sadat valadie some; Mahdeian, Ali

doi:10.1109/icciautom.2011.6356825

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…On one hand, the number of children born into a family has significant implications for the physiological well-being of the mother's reproductive system and her overall health (2). Birth and fertility rates are pivotal variables when examining the evolution of human behavior, a phenomenon influenced by numerous demographic, social, and economic factors in society (6,7). Understanding the factors shaping reproductive preferences and trends enables us to predict fertility patterns and the number of children while effectively managing population growth.…”

Section: Introductionmentioning

confidence: 99%

Effective Factors in the Number of Children Among Iranian Women: An Application of Poisson Regression Model

Ghorbani,

Gharibi,

Ansari-Nia

et al. 2023

Middle East J Rehabil Health Stud

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Background: The number of children born into a family has a significant impact on a mother’s reproductive system’s physiological health and overall well-being. Objectives: This study aimed to explore the effective factors in the number of living children (NLC) among married women in Semnan, Iran, with a particular focus on social, cultural, and economic influences. Methods: This cross-sectional study examined the reproductive histories of 600 married women aged 15 to 49 years from Semnan, Iran. Sample size calculations were based on the principal variable, the expected number of children, using PASS software. The women were selected through a multistage random sampling method from health center lists in 2018 (April-October). The data were collected through interview questionnaires and analyzed using the Quasi-Poisson model. Results: The mean (standard deviation [SD]) age at first marriage for mothers (MAM) and fathers was estimated to be 21.02 (4.80) and 25.10 (4.70) years, respectively. Additionally, the mean (SD) values for the number of expected children (NCEX), living children, and the number of pregnancies were 2.19 (0.96), 1.85 (0.81), and 2.15 (1.03), respectively. Among the variables of interest, only NCEX, the number of pregnancies (with a positive effect), MAM, father’s education, multiple births, and the desire to have children (with a negative effect) significantly influenced NLC. Conclusions: This study recommends implementing an educational program to promote an optimal and ideal family size to prevent adverse pregnancy outcomes. The age of women at marriage is a significant factor, as an increase in women’s age leads to shorter pregnancy intervals, exposing women to complications, such as premature birth, perinatal death, and intrauterine growth restriction. Policymakers should, therefore, encourage early marriages by fostering a culture and providing supportive facilities.

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Section: Introductionmentioning

confidence: 99%

Effective Factors in the Number of Children Among Iranian Women: An Application of Poisson Regression Model

Ghorbani,

Gharibi,

Ansari-Nia

et al. 2023

Middle East J Rehabil Health Stud

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“…To predict the surface roughness, linear regression is employed. Wavelet based defect detection is discussed in [6]. The textural features are extracted by using wavelets to detect hole, scrape, corrosion and wrinkle.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Quality Control System by Machine Learning for Surface Defects

Alam¹,

Mohanan²

2021

IJASIS

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Quality control plays a crucial role to meet the high and accurate quality of production in many manufacturing industries. The high quality products may become unreliable due to surface defects. Generally, quality control is more important in automotive industry such as in the field of carbody parts manufacturing. The exterior appearance of the car body should be smooth surfaces and edges with flawless nature. In order to build such flawless body parts, surface defect detection system is taken into account. In this paper, an Automated Defect Detection (ADD) system is presented. The design of the ADD system consists of two steps. The first step is considered as a classification system where the given image is classified into defected or nondefected using Gabor expansion with Principal Component Analysis (PCA). The next step is segmentation where the region of defect is identified using local thresholding. The evaluation is performed on raw alloy steel surface and machined surfaces of steel and cast iron. Results prove that the ADD system classify the input image into defect/no defect with 100% accuracy by a simple nearest neighbor classifier and with 94.5% detection accuracy for the segmentation system.

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“…exceeded 92%, markedly enhancing classification precision. In 2011, Sadeghi 4 et al devised a technique that swiftly and accurately identifies such defects by employing two-dimensional wavelets for normalized and nonnormalized assessments of local image values, confined within the limit of [0,1]. Through the normalization of resulting partial image variances and the incorporation of variance variation values with relevant partial images, this approach outperforms other methods in terms of correct detection of defective areas.…”

mentioning

confidence: 99%

Research on steel surface defect classification method based on deep learning

Gao,

Lv,

Xiao

et al. 2024

Sci Rep

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Surface defects on steel, arising from factors like steel composition and manufacturing techniques, pose significant challenges to industrial production. Efficient and precise detection of these defects is crucial for enhancing production efficiency and product quality. In accordance with these requisites, this paper elects to undertake the detection task predicated on the you only look once (YOLO) algorithm. In this study, we propose a novel approach for surface flaw identification based on the YOLOv5 algorithm, called YOLOv5-KBS. This method integrates attention mechanism and weighted Bidirectional Feature Pyramid Network (BiFPN) into YOLOv5 architecture. Our method addresses issues of background interference and defect size variability in images. Experimental results show that the YOLOv5-KBS model achieves a notable 4.2% increase in mean Average Precision (mAP) and reaches a detection speed of 70 Frames Per Second (FPS), outperforming the baseline model. These findings underscore the effectiveness and potential applications of our proposed method in industrial settings.

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Application of two dimensional wavelet for defect detection in steel process

Cited by 5 publications

References 8 publications

Effective Factors in the Number of Children Among Iranian Women: An Application of Poisson Regression Model

Effective Factors in the Number of Children Among Iranian Women: An Application of Poisson Regression Model

An Efficient Quality Control System by Machine Learning for Surface Defects

Research on steel surface defect classification method based on deep learning

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