scholarly journals Metallic Surface Coating Defect Detection Using Firefly Based Adaptive Thresholding and Level Set Method

2019 ◽  
Vol 8 (10) ◽  
pp. 3698-3704
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Defect Detection ◽  
Surface Defects ◽  
Optimization Problems ◽  
Adaptive Thresholding ◽  
Metallic Surface ◽  
Nature Inspired Algorithm ◽  
Varied Size ◽  
Processing Techniques

An innovative approach is introduced to detect surface defects on titanium coated steel surfaces with varied size through the use of image processing techniques. This paper provides techniques which are useful to discover numerous kinds of surface defects present on coating surface. For defect detection, Firefly Algorithm (FA) based adaptive thresholding is proposed and is applied for the gray scale images. The FA ensuing nature inspired algorithm utilized expansively in support of determining various optimization problems and from the reconstructed image contours are extracted using level set method, the predictable images not including textures besides defects contours be compassed. The morphological post processing removes the noise in image and makes defects more distinguishable from the background. The speculative result persists in utilizing synchronous images of metal surface and shows that the proposed method can efficiently segment surface defects and obtain better performance than existing methods.

Image-based Surface Defect Detection Using Deep Learning: A Review

2021 ◽  
pp. 1-23
Author(s):  
Prahar Bhatt ◽  
Rishi K. Malhan ◽  
Pradeep Rajendran ◽  
Brual Shah ◽  
Shantanu Thakar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defects ◽  
Future Research ◽  
Specific Class ◽  
Survey Paper ◽  
Context Learning ◽  
Surface Defect Detection ◽  
Manufacturing Applications ◽  
Processing Techniques

Abstract Automatically detecting surface defects from images is an essential capability in manufacturing applications. Traditional image processing techniques were useful in solving a specific class of problems. However, these techniques were unable to handle noise, variations in lighting conditions, and background with complex textures. Increasingly deep learning is being explored to automate defect detection. This survey paper presents three different ways of classifying various efforts. These are based on defect detection context, learning techniques, and defect localization and classification method. The existing literature is classified using this methodology. The paper also identifies future research directions based on the trends in the deep learning area.

A Review Paper on Product Surface Defect Detection of Ironing Process

2016 ◽  
Vol 836 ◽  
pp. 147-152
Author(s):  
Akhmad Faizin ◽  
Arif Wahjudi ◽  
I. Made Londen Batan ◽  
Agus Sigit Pramono
Keyword(s):  
Image Processing ◽  
Surface Roughness ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Forming Process ◽  
Metal Forming Process ◽  
Surface Defect Detection ◽  
Product Surface ◽  
Processing Techniques

The quality of product of manufacturing industries depends on dimension accurately and surface roughness quality. There are many types of surface defects and levels of surface roughness quality. Ironing process is one type of metal forming process, which aims to reduce the wall thickness of the cup-shaped or pipes products, thus increasing the height of the wall. Manually surface inspection procedures are very inadequate to ensure the surface in guaranteed quality. To ensure strict requirements of customers, the surface defect inspection based on image processing techniques has been found to be very effective and popular over the last two decades. The paper has been reviewed some papers based on image processing for defect detection. It has been tried to find some alternatives of useful methods for product surface defect detection of ironing process.

scholarly journals COUPLING BOUNDARY ELEMENT METHOD WITH LEVEL SET METHOD TO SOLVE INVERSE PROBLEM

2017 ◽  
Vol 7 (1) ◽  
pp. 80-83 ◽  
Author(s):  
Tomasz Rymarczyk ◽  
Paweł Tchórzewski ◽  
Jan Sikora
Keyword(s):  
Inverse Problem ◽  
Boundary Element Method ◽  
Boundary Element ◽  
Level Set Method ◽  
Level Set ◽  
Optimization Problems ◽  
Adjoint Equation ◽  
Iteration Step ◽  
Boundary Perturbations ◽  
Element Method

The boundary element method and the level set method can be used in order to solve the inverse problem for electric field. In this approach the adjoint equation arises in each iteration step. Results of the numerical calculations show that the boundary element method can be applied successfully to obtain approximate solution of the adjoint equation. The proposed solution algorithm is initialized by using topological sensitivity analysis. Shape derivatives and material derivatives have been incorporated with the level set method to investigate shape optimization problems. The shape derivative measures the sensitivity of boundary perturbations. The coupled algorithm is a relatively new procedure to overcome this problem. Experimental results have demonstrated the efficiency of the proposed approach to achieve the solution of the inverse problem.

Weld defect detection on digital radiographic image using level set method

2013 ◽  
Author(s):  
Suhaila Abd Halim ◽  
Bertha Trissan Petrus ◽  
Arsmah Ibrahim ◽  
Yupiter HP Manurung ◽  
Mohd Idris Jayes
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Defect Detection ◽  
Radiographic Image ◽  
Weld Defect

scholarly journals USING ELECTRICAL IMPEDANCE TOMOGRAPHY IN LINEAR ARRAYS OF MEASUREMENT

2017 ◽  
Vol 7 (1) ◽  
pp. 76-79
Author(s):  
Tomasz Rymarczyk
Keyword(s):  
Topology Optimization ◽  
Electrical Impedance Tomography ◽  
Level Set Method ◽  
Level Set ◽  
Electrical Impedance ◽  
Optimization Problems ◽  
Measurement Model ◽  
Impedance Tomography ◽  
Material Derivative ◽  
Level Set Function

The article presents an application to the topology optimization in electrical impedance tomography using the level set method. The level set function is based on shape and topology optimization for areas with partly continuous conductivities. The finite element method has been used to solve the forward problem. The proposed algorithm is initialized using topological sensitivity analysis. Shape derivative and material derivative have been incorporated with the level set method to investigate shape optimization problems. The coupled algorithm is a relatively new procedure to overcome this problem. Using the line measurement model is very useful to solve the inverse problem in the copper-mine ceiling and the flood embankment.

An Energy-Minimizing Level Set Method for Defect Detection

2018 ◽  
Vol 102 (4) ◽  
pp. 3545-3555
Author(s):  
Kun Hu ◽  
Shuyou Zhang ◽  
Xinyue Zhao
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Defect Detection
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