An Efficient Technique for Identifying Defects in Stainless Steel (SS) Plate using Image Processing

Elanangai, V.; Vasanth, K.

doi:10.1109/icaccs54159.2022.9784982

Cited by 2 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another study using edge detection filters such as LoG, canny, roberts, perwitt and sobel in the analysis phase, SSIM (Structural Symmetry Indicator Matrix), MSE (Mean Square Error), IEF (Image Enhancement Factor) and PSNR (Peak Signal to Noise Ratio) parameters were calculated. In the analysis, the optimized k-Winsorized showed optimal results for the average noise removal capacity, and this analysis has proven to be an effective technique for detecting defects in stainless steel plates [11].…”

Section: Introductionmentioning

confidence: 93%

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

Feyzioğlu

Taşpınar

2023

International Journal of Applied Mathematics Electronics and Computers

View full text Add to dashboard Cite

Iron metal is the most widely used metal type. This metal, which is used in countless sectors, is processed in different ways and turned into steel. Since steel has a brittle structure compared to iron, defects may occur in the plates during the rolling process. Detection of these defects at the production stage is of great importance in terms of commercial and safety. Machine learning methods can be used in such problems for fast and high accuracy detection. For this purpose, using a dataset obtained from stainless steel surface defects in this study, classification processes were carried out to detect defects with four different machine learning methods. Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for classification processes. The highest classification accuracy was obtained from the 79.44% RF model. Correlation analysis was performed in order to analyze the effects of the features in the dataset on the classification results. It is thought that the classification accuracy of the proposed models is satisfactory for this challenging problem, but needs to be upgraded.

show abstract

Section: Introductionmentioning

confidence: 93%

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

Feyzioğlu

Taşpınar

2023

International Journal of Applied Mathematics Electronics and Computers

View full text Add to dashboard Cite

show abstract

Recent Advances in Selection Techniques for Image Processing

Sathiyaraj¹,

Ramachandran²,

Vidhya³

2022

EAE

View full text Add to dashboard Cite

The parameters and modifying the code, the library allows students in image processing to learn practical methods. In addition, in addition to teaching programming in the "turtle graphics" paradigm, such as color and dimension and to introduce users to image ideas A new module is provided. Online gallery of examples, in addition to providing an overview of the available activity, commonly used in image processing Introduces several algorithms. These usually include an introduction to the package and an insight, for image processing ideas Provides introductions. Well documented application programming interface (API) contributes to the learning experience with tools that facilitate visualization, It also makes it easier to explore the effect of various algorithms and parameters. So, it is not surprising that there are so many Image processing algorithms for margin extraction, upgrade, rearrangement; data compression, etc. are unambiguous. Artifacts can also be introduced through digital image processing such as margin enhancement. Since artifacts can prevent diagnosis or provide incorrect measurements, it is important to avoid them or at least understand their appearance. It is clear that a pattern independent of the spatial size or scale of image features is required and only emphasizes the range of less-contrasting features. Diversified image processing has been extensively studied not only by computer scientists but also by neurophysiologists, and the approach to improving this image is currently being used in a clinical way. It is well known that the human visual system uses a multifaceted approach.

show abstract

An Efficient Technique for Identifying Defects in Stainless Steel (SS) Plate using Image Processing

Cited by 2 publications

References 9 publications

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

Recent Advances in Selection Techniques for Image Processing

Contact Info

Product

Resources

About