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

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

Scientific Research Journal ◽

10.24191/srj.v9i1.9412 ◽

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 ◽

Different Types ◽

Morphological Transformations ◽

Non Destructive

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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RADIOGRAPHIC IMAGE ENHANCEMENT USING HYBRID ALGORITHM

Jurnal Teknologi ◽

10.11113/jt.v78.9089 ◽

2016 ◽

Vol 78 (6-7) ◽

Author(s):

Varin Chouvatut ◽

Ekkarat Boonchieng

Keyword(s):

Image Quality ◽

Spatial Resolution ◽

Computed Radiography ◽

Median Filter ◽

Correct Diagnosis ◽

Image Contrast ◽

Radiographic Image ◽

Radiographic Image Enhancement ◽

Radiographic Images ◽

Low Contrast

Radiographic image quality is important in the medical field since it can increase the visibility of anatomical structures and even improve the medical diagnosis. Because the image quality depends on contrast, noise, and spatial resolution, images with low contrast, a lot of noises, or low resolution will decrease image quality, leading to an incorrect diagnosis. Therefore, radiographic images should be enhanced to facilitate medical expertise in making correct diagnosis. In this paper, radiographic images are enhanced by hybrid algorithms based on the idea of combining three image processing techniques: Contrast Limited Adaptive Histogram Equalization for enhancing image contrast, Median Filter for removing noises, and Unsharp Masking for increasing spatial resolution. Two series of medical images consisting of 20 x-ray images and 20 computed radiography images are enhanced with this method. Peak Signal to Noise Ratio (PSNR) and image contrast are computed in order to measure image quality. The results indicate that the enhanced images have better PSNR.

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Development of Radiographic Image Classification System for Weld Defect Identification using Deep Learning Technique

International Journal of Scientific and Engineering Research ◽

10.14299/ijser.2021.05.01 ◽

2021 ◽

Vol 12 (5) ◽

pp. 390-394

Author(s):

Distun Stephen ◽

Dr.Lalu P.P

Keyword(s):

Neural Network ◽

Deep Learning ◽

Neural Network Model ◽

Classification System ◽

Radiographic Image ◽

Radiographic Images ◽

Defect Identification ◽

Weld Defect ◽

Learning Technique ◽

Welding Defect

Weld defect identification from radiographic images is a crucial task in the industry which requires trained human experts and enough specialists for performing timely inspections. This paper proposes a deep learning based approach to identify different weld defects automatically from radiographic images. To employ this a dataset containing 200 radiographic images labelled for four types of welding defect- gas pore, cluster porosity, crack and tungsten inclusion is developed. Then a Convolutional Neural Network model is designed and trained using this database.

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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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Triboelectric Nanogenerator-Based Self-Powered Resonant Sensor for Non-Destructive Defect Detection

Sensors ◽

10.3390/s19153262 ◽

2019 ◽

Vol 19 (15) ◽

pp. 3262 ◽

Cited By ~ 3

Author(s):

Jinkai Chen ◽

Chi Zhang ◽

Weipeng Xuan ◽

Liyang Yu ◽

Shurong Dong ◽

...

Keyword(s):

Defect Detection ◽

Triboelectric Nanogenerator ◽

Force Frequency ◽

Resonant Frequencies ◽

Resonant Sensor ◽

Different Types ◽

New Type ◽

Self Powered ◽

Non Destructive ◽

Frequency Distance

A triboelectric nanogenerator-based self-powered resonant sensor is proposed and investigated. By integrating an inductor and a microswitch with a triboelectric nanogenerator, a new type triboelectric nanogenerator is obtained, the pulse voltage output is converted to an oscillating signal with a very stable modulated resonant frequency, immune to the cross disturbance of contact-related variation (force, frequency, distance) and environmental variation, such as humidity and temperature. This is utilized for non-destructive defect detection. When the coil inductor scans the surface of a specimen with defects, varying resonant frequencies are obtained for different types of defects, showing excellent consistency between the experimental and simulated results. The results demonstrate the potential of the self-powered TENG-based resonant sensor to be a highly stable and sensitive magnetic sensor for the non-destructive defect detection applications.

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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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Research of Radiographic Image Enhancement Technology

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.760-762.1414 ◽

2013 ◽

Vol 760-762 ◽

pp. 1414-1417

Author(s):

Yan Chen ◽

Yan Ling Shao ◽

Zhi Guo Gui

Keyword(s):

Reference Value ◽

Local Area ◽

Local Dynamic ◽

Coefficient Function ◽

Radiographic Image ◽

Radiographic Image Enhancement ◽

Radiographic Images ◽

Low Contrast ◽

Dynamic Enhancement ◽

New Algorithms

mage enhancement has applied widely in biomedical, nondestructive testing, satellite remote sensing and other fields. Especially for the low contrast radiographic images, usually there are some disadvantages for a radiographic image such as the local area image does not show a striking contrast. In order to improve the clearness of low contrast radiographic images, in this paper we combined global adaptive equalization with local dynamic enhancement,then we simulate this enhancement algorithm. The new method will not only effectively increase the global contrast of low contrast radiographic images, but also intensify local details. Because the new algorithms contrast enhancement coefficient function can be adjusted dynamically and locally, the new algorithms are not only adaptive to the process of radiographic images but also having great reference value to the other grayscale images.

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Application of USCCD on Girth Weld Defect Detection of Oil Pipelines

Applied Sciences ◽

10.3390/app10082736 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2736 ◽

Cited By ~ 1

Author(s):

L. S. Dai ◽

Q. S. Feng ◽

X. Q. Xiang ◽

J. Sutherland ◽

T. Wang ◽

...

Keyword(s):

Defect Detection ◽

Oil And Gas ◽

Primary Concern ◽

Oil Pipeline ◽

Pull Test ◽

Weld Defect ◽

Ultrasonic Propagation ◽

Girth Welds ◽

External Loads ◽

Non Destructive

Globally, more and more attention has been paid to the integrity of Girth Welds (GW) of oil and gas pipelines due to their failures with high consequences. A primary concern is that defects originate during field construction but over time may be subject to external loads due to earth movement. GW defects in newly built pipelines are also assumed to exist but would be much smaller in size, and more difficult to detect, which motivated the investigation into minimum defect detection capabilities of the inspection technologies. This study presents the evaluation results of UltraScan™ Circumferential Crack-Like Detection (USCCD) technology for oil pipeline GW inspection, based upon the pull test and in field data from Inline Inspection (ILI) of pipeline by PetroChina Pipeline Company (PPC) using GE PII (General Electric Company, Pipeline Integrity Inspection) 32” UltraScan™ CCD Tool. The performance of USCCD is given according to the ILI data, pull test results and dig NDE (Non-Destructive Examination). It can be concluded that crack-like defects with clear edges can be detected during ultrasonic propagation; however, the irregular shape of weld makes the inspection more difficult. It is still a challenge to identify the type of defects, and depth sizing can only be classified not quantified, which would require more excavations. However, this technology is feasible for the alternative technology of GW defect inspection.

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