Improving the robustness of DI and PVI further using fast guided filter on radiographic images

2021 ◽  
Vol 63 (7) ◽  
pp. 409-415
Author(s):  
Changying Dang ◽  
Jiansu Li ◽  
Zhiqiang Zeng ◽  
Wenhua Du ◽  
Rijun Wang
Keyword(s):  
Uniform Distribution ◽  
Guided Filter ◽  
Defect Index ◽  
Radiographic Images ◽  
Irregular Surfaces ◽  
Radiographic Testing ◽  
Desirable Outcome ◽  
Calculational Method ◽  
Weld Defect ◽  
Improvement Method

To further improve the robustness of the weld defect index (DI) and peak-valley index (PVI), which are key indices for detecting weld defects in radiographic testing (RT) images accurately and reliably, a robust improvement method is proposed, in which a fast guided filter (Fast-GF) is introduced and its effect on the DI and PVI is analysed. In this paper, the principle of the proposed robust improvement method, the related theory of Fast-GF, the definition and the calculational method of the DI and PVI are systematically analysed. Taking some practical RT images from industrial welding as an example, smoothing experiments with different filters and comparative computational experiments for the DI and PVI both with and without Fast-GF are carried out. The experimental results show that the robustness of the DI and PVI is further improved by the proposed robust improvement method, which is a desirable outcome. More specifically, the values of the DI and PVI are computed accurately and reliably regardless of some non-uniform distribution of grey levels, noise, irregular surfaces and artefacts in the RT images.

A novel method of text detection in radiographic images

2019 ◽  
Vol 61 (10) ◽  
pp. 591-596 ◽  
Author(s):  
Yasheng Chang ◽  
Jianmin Gao ◽  
Hongquan Jiang
Keyword(s):  
Nuclear Power ◽  
Rapid Development ◽  
Text Detection ◽  
Radiographic Images ◽  
Material Resources ◽  
Radiographic Testing ◽  
Significant Efficacy ◽  
Important Means ◽  
Novel Method ◽  
Frequency Domain Filtering

With the rapid development of industries such as nuclear power and shipbuilding, radiographic testing (RT) is widely used in these fields as an important means of weld inspection. It also produces a large number of radiographic films, which consume a great deal of manpower and material resources. It is therefore beneficial for the radiographic film to be digitised for storage and archiving. Text detection in RT weld images is an important prerequisite for the archiving of digitised films. This paper proposes a novel text detection method that employs mask convolution and frequency-domain filtering, which can detect text at different positions, with different fonts and of different sizes in RT weld images. The method is evaluated using 366 different images and shows significant efficacy for text detection in RT weld images, with the precision value reaching 96%. The method used in this paper is also compared with other methods that are commonly used in other fields and the results show that the proposed method gives improved results compared to state-of-the-art methods.

Cracking of Heavy Wall Steam Separators During Fabrication: An Investigation

2012 ◽  
Author(s):  
Dilip Kumar ◽  
Luis Ganhao
Keyword(s):  
Cross Section ◽  
High Hardness ◽  
Radiographic Testing ◽  
Weld Defect ◽  
Section Viii ◽  
Carbon Arc ◽  
Recent Project ◽  
Weld Area ◽  
Non Destructive

On a recent project, four high pressure steam separator vessels were received from overseas after fabrication. There was suspicion on the quality of fabrication when non destructive examination (NDE) reports were reviewed. There were major concerns with the quality of radiographic films as they did not meet the ASMe Section VIII Div. 1 Code requirements as well as client specifications. Subsequent examination of welds using radiographic testing (RT) revealed crack-like features around nozzles in the region adjoining (but outside) the weld metal. Macro etching at the surface around nozzles showed that the weld area was extended beyond the apparent weld/base metal interface. Further examination of a cross section cut out from one vessel nozzle confirmed the initial doubts that weld repairs had been performed that were not reported. Metallography of the cross section indicated evidence of significant cracking associated with carbon contamination and very high hardness (up to 365 HV; in one particular case 609 HV) in affected areas. This was believed to be due to improper and incomplete cleaning by grinding after performing carbon arc or, flame gouging to remove a weld defect. Further detailed NDE was carried out using advanced ultrasonic testing (UT), i.e. phased array UT and time of flight diffraction (TOFD) and all defects (many new that were undetected by RT) were repaired per ASME Section VIII Div. 1 Code and client specification. This experience was a lesson for the design office and helped make a decision to be much more vigilant and to ask for greater quality surveillance on overseas fabrication of critical equipment for all future projects. The paper discusses the detailed investigation as well as findings.

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

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. 

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

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.

Classification of weld defect based on information fusion technology for radiographic testing system

2016 ◽  
Vol 87 (3) ◽  
pp. 035110 ◽  
Author(s):  
Hongquan Jiang ◽  
Zeming Liang ◽  
Jianmin Gao ◽  
Changying Dang
Keyword(s):  
Information Fusion ◽  
Testing System ◽  
Fusion Technology ◽  
Radiographic Testing ◽  
Weld Defect

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

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
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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