Rapid surface defect detection based on singular value decomposition using steel strips as an example

Sun, Qianlai; Wang, Yin; Sun, Zhigang

doi:10.1063/1.5017589

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Factorization in Equation 1is the SVD of the matrix A. The columns in U and V represent left singular vector and right singular vector of A, respectively (Sun et al, 2018).…”

Section: Empirical Orthogonal Function Methods Based On Singular Value Decompositionmentioning

confidence: 99%

Spatial and Temporal Analysis of El Niño Impact on Land and Forest Fire in Kalimantan and Sumatra

2021

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Land and forest fires in Kalimantan and Sumatra, Indonesia occurred annually at different magnitude and duration. Climate and sea interaction, like El Niño, influences the severity of dry seasons preceding the fires. However, research on the influence of El Niño intensity to fire regime in Kalimantan and Sumatra is limited. Therefore, this study aims to analyze the spatial and temporal patterns of the effects of El Niño intensity on land and forest fires in fire-prone provinces in Indonesia. Here, we applied the empirical orthogonal function analysis based on singular value decomposition to determine the dominant patterns of hotspots and rainfall data that evolve spatially and temporally. For analysis, the study required the following data: fire hotspots, dry-spell, and rainfall for period 2001-2019. This study revealed that El Niño intensity had a different impacts for each province. Generally, El Niño will influence the severity of forest fire events in Indonesia. However, we found that the impact of El Niño intensity varied for Kalimantan, South Sumatra, and Riau Province. Kalimantan was the most sensitive province to the El Niño event. The duration and number of hotspots in Kalimantan increased significantly even in moderate El Niño event. This was different for South Sumatra, where the duration and number of hotspots only increased significantly when a strong El Niño event occurred.

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“…Factorization in Equation 1is the SVD of the matrix A. The columns in U and V represent left singular vector and right singular vector of A, respectively (Sun et al, 2018).…”

Section: Empirical Orthogonal Function Methods Based On Singular Value Decompositionmentioning

confidence: 99%

Spatial and Temporal Analysis of El Niño Impact on Land and Forest Fire in Kalimantan and Sumatra

2021

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“…The factorization in Equation ( 1) is the SVD of matrix A. The columns in U and V represent the left singular vector and the right singular vector of A, respectively [47].…”

Section: Empirical Orthogonal Function Based On Covariance Singular V...mentioning

confidence: 99%

Joint Pattern Analysis of Forest Fire and Drought Indicators in Southeast Asia Associated with ENSO and IOD

2022

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Land and forest fires in Southeast Asia often coincide with severe dry seasons in the specific region caused by the warm phase of an El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). This research aims to identify and quantify the ENSO–IOD effect on a joint pattern between the dry season and land and forest fires in Southeast Asia. This research provides a quantitative result which represents the ENSO–IOD’s impact in Southeast Asia for hotspots, dry spells, and precipitation anomalies. The methods used in this research are singular value decomposition, probability density skill score, and coherence analysis. Cambodia, Myanmar, and Thailand gave a similar result with less than a 25% increasing severity in the hotspots from normal to either El Niño, positive IOD, or El Niño–positive IOD years. The maximum increase in hotspot severity in North Sumatra was 13.06% and happened during a weak El Niño and positive IOD. Meanwhile, South Sumatra had a maximum accumulation of more than 89% and Kalimantan had more than a 72% increase during the strong El Niño in 2015. Even though the relationship between the ENSO and IOD was inconsistent, the occurrence of both phenomena in the same year can lead to fires and need to be considered.

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“…Cao Binfang et al proposed a defect detection method based on spatial-frequency multi-scale block local binary pattern to solve the problem of complex geometry and texture distribution of the nickel foam surface defect images [14]. Sun qianlai uses singular value decomposition to identify and locate surface defects of strip steel without image segmentation [15]. Based on the research on pseudo defect elimination, patch texture description and adaptive threshold segmentation, Liu Kun et al proposed a new unsupervised steel surface defect detection model based on Haar-Weibullvariance [16].…”

Section: Related Workmentioning

confidence: 99%

Cylinder Liner Defect Detection and Classification based on Deep Learning

Gao¹,

Fei²,

Song³

et al. 2022

IJACSA

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The machine vision-based defect detection for cylinder liner is a challenging task due to irregular shape, various and small defects on the cylinder liner surface. To improve the accuracy of defect detection by machine vision a deep learning-based defect detection method for cylinder liner was explored in this paper. First, a machine vision system was designed based on the analysis of the causes and types of defects to obtain the field images for establishing an original dataset. Then the dataset was augmented by a modified augmentation method which combines the region of interest automatic extraction method with the traditional augmentation methods. Except for introduction of the anchor configuration optimization method, an XML file-based method of highlighting defect area was proposed to address the problem of tiny defect detection. The optimal model was experimentally determined by considering the network model, the training strategy and the sample size. Finally, the detection system was developed and the network model was deployed. Experiments are carried out and the results of the proposed method compared with those of the traditional methods. The results show that the detection accuracies of sand, scratch and wear defects are 77.5%, 70% and 66.3% which are improved by at least 26.3% compared with the traditional methods. The proposal can be used for field defect detection of cylinder liner.

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Rapid surface defect detection based on singular value decomposition using steel strips as an example

Cited by 4 publications

References 14 publications

Spatial and Temporal Analysis of El Niño Impact on Land and Forest Fire in Kalimantan and Sumatra

Spatial and Temporal Analysis of El Niño Impact on Land and Forest Fire in Kalimantan and Sumatra

Joint Pattern Analysis of Forest Fire and Drought Indicators in Southeast Asia Associated with ENSO and IOD

Cylinder Liner Defect Detection and Classification based on Deep Learning

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