Weld defect detection on digital radiographic image using level set method

Accurate inspection of welded materials is important in relation to achieve acceptable standards. Radiography, a non-destructive test method, is commonly used to evaluate the internal condition of a material with respect to defect detection. The presence of noise in low resolution of radiographic images significantly complicates analysis; therefore attaining higher quality radiographic images makes defect detection more readily achievable. This paper presents a study pertaining to the quality enhancement of radiographic images with respect to different types of defects. A series of digital radiographic weld flaw images were smoothed using multiple smoothing techniques to remove inherent noise followed by top and bottom hat morphological transformations. Image quality was evaluated quantitatively with respect to SNR, PSNR and MAE. The results indicate that smoothing enhances the quality of radiographic images, thereby promoting defect detection with the respect to original radiographic images.

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Weld Inspection Based on Radiography Image Segmentation with Level Set Active Contour Guided Off-Center Saliency Map

Advances in Materials Science and Engineering ◽

10.1155/2015/871602 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Mohamed Ben Gharsallah ◽

Ezzeddine Ben Braiek

Keyword(s):

Level Set ◽

Defect Detection ◽

Active Contour ◽

Salient Feature ◽

Saliency Map ◽

Radiographic Images ◽

Low Contrast ◽

Small Defect ◽

Weld Defect ◽

Image Pixels

Radiography is one of the most used techniques in weld defect inspection. Weld defect detection becomes a complex task when uneven illumination and low contrast characterize radiographic images. In this paper we propose a new active contour based level set method for weld defect detection in radiography images. An off-center saliency map exploited as a feature to represent image pixels is embedded into a region energy minimization function to guide the level set active contour to defects boundaries. The aim behind using salient feature is that a small defect can frequently attract attention of human eyes which permits enhancing defects in low contrasted image. Experiment results on different weld radiographic images with various kinds of defects show robustness and good performance of the proposed approach comparing with other segmentation methods.

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Automatic weld defect detection based on potential defect tracking in real-time radiographic image sequence

NDT & E International ◽

10.1016/j.ndteint.2011.10.008 ◽

2012 ◽

Vol 46 ◽

pp. 14-21 ◽

Cited By ~ 25

Author(s):

Jiaxin Shao ◽

Dong Du ◽

Baohua Chang ◽

Han Shi

Keyword(s):

Real Time ◽

Defect Detection ◽

Image Sequence ◽

Radiographic Image ◽

Weld Defect

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Digital Radiographic Image Enhancement for Weld Defect Detection using Smoothing and Morphological Transformations

Scientific Research Journal ◽

10.24191/srj.v9i1.5053 ◽

2012 ◽

Vol 9 (1) ◽

pp. 15

Author(s):

Suhaila Abdul Halim ◽

Arsmah Ibrahim ◽

Yupiter Harangan Prasada Manurung

Keyword(s):

Defect Detection ◽

Test Method ◽

Radiographic Image ◽

Radiographic Image Enhancement ◽

Radiographic Images ◽

Weld Defect ◽

Internal Condition ◽

Different Types ◽

Morphological Transformations ◽

Non Destructive

Accurate inspection ofweldedmaterials is important in relation to achieve acceptable standards. Radiography, a non-destructive test method, is commonly used to evaluate the internal condition ofa material with respect to defect detection. Thepresence ofnoise in low resolution ofradiographic images significantly complicates analysis; thereforeattaining higher quality radiographic images makes defect detection more readily achievable. This paper presents a study pertaining to the quality enhancement of radiographic images with respect to different types of defects. A series of digital radiographic weld flaw images were smoothed using multiple smoothing techniques to remove inherent noise followed by top and bottom hat morphological transformations. Image quality was evaluated quantitatively with respect to SNR, PSNR andMAE. The results indicate that smoothing enhances the quality ofradiographic images, thereby promoting defect detection with the respect to original radiographic images.

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

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9666.0881019 ◽

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.

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