Automated thresholding in radiographic image for welded joints

International Journal of Computer Applications, 2010

Digital Image Analysis is one of the most challenging and important tasks in many scientific and engineering applications. Extracting the Region of Interest (ROI) from the image and recognition in image processing are very important steps. When these tasks are manually performed, it is tedious and difficult involving human experts. This paper focuses on automatic defect detection and counting in radiographic weldment images thus considering defects in weldment images as object of interest. To detect defects in radiographic weldment images, thresholding and segmentation algorithm is used and a new procedure is introduced for counting number of defects in the input images. The results obtained from the proposed work are impressive with respect to the computational time and defect detection rate. The performance of the proposed algorithm is found better than the existing defect detection algorithms.

2017

In this paper, a Computer Aided Defect Detection (CADD) application is developed to ai a human operator interpret radiographs of weld joints . Conventional radiography interpretation is highly dependent on operator skill and hence prone to human errors. This algorithm is developed to automate defect recognition in radiographed images. This application consists of several image processing al gorithms like Contrast Limited Adaptive Histogram Equalization, Sauvola thresholding, Canny edge operations an d Boundary Tracing Algorithms for defect segmentation and characterization. Geometric features of s gmented regions are used t o classify a region into a particular type of defect. A CPU based approach is used for defect identification. The application has been tested on Aluminium TIG welded specimens with well d efine defects. This application provides successful detection rate of 88% for porosities, 100% for Tungsten Inclusions and 100% probability in detecting cracks and undercuts. Th...

NDT & E International, 2006

It is necessary to detect suspected defect regions in the radiographic weld images to find the type of flaw and its causative factors. This requires processing of radiographic images by a suitable approach. This paper presents an approach to process these radiographic weld images of the weld specimens considering morphological aspects of the image. The proposed approach first determines the flaw boundaries by applying the Canny operator after choosing an appropriate threshold value. The boundaries are then fixed using a morphological image processing approach i.e. dilating few similar boundaries and eroding some irrelevant boundaries decided on the basis of pixel characteristics. The flaws detected by this approach are categorized according to their properties. q

2016

In this paper, we are interested to segment weld radiographic images using the level set method (LSM) based on kernel fuzzy c-means clustering (KFCM) in order to extract the region of interest (weld defects) and to improve the precision of segmentation. The proposed approach contains two successive necessary stages. The first one consists in the application of kernel fuzzy c-means algorithm to get a clustered image. The second stage is based on the using of the appropriate class of the clustered image as an initial contour of the level set method to extract the defects boundaries. The experimental results have shown that the proposed model can extract successfully the interest region from image and confirm its efficiency for welding defects segmentation.

Ndt & E International, 2002

Radiographic testing is a well-established non-destructive testing method to detect subsurface welding defects. In this paper, an automatic computer-aided identification system was implemented to recognize different types of welding defects in radiographic images. Imageprocessing techniques such as background subtraction and histogram thresholding were implemented to separate defects from the background. Twelve numeric features were extracted to represent each defect instance. The extracted feature values are subsequently used to classify welding flaws into different types by using two well-known classifiers: fuzzy k-nearest neighbor and multi-layer perceptron neural networks classifiers. Their performances are tested and compared using the bootstrap method. q

Insight - Non-Destructive Testing and Condition Monitoring, 2007

International Journal of Reasoning-based Intelligent Systems, 2011

A new approach for the automatic detection of welding defects in digital images (X-ray or photos) is presented in this paper. Various algorithms used in the consecutive stages of the image processing-permitting the successful detection of different kinds of defects and ensuring high accuracy-are described. The method is suitable for the analysis of images of pipes, plates, etc. The experimental results obtained with the software implementation of the described algorithms prove the efficiency and reliability of the new approach. This paper points up the advantages of the presented algorithms in comparison with some well-known methods for non-destructive welding control.

NDT International, 1990

This paper describes a real-time radiography configuration for the automatic inspection of welds. The optimal geometrical imaging configuration is evaluated and discussed in relation to conventional film radiography. For the automatic inspection of X-ray images, a two-step analysis was adopted: a fast search for defective regions, followed by fine identification and location of defects. Two different algorithms, based on the relative irregular behaviour of a defect, were develoPed for the fast search procedure. The second step, fine identification, can be achieved by a sequential similarity detection algorithm or by a thresholding algorithm. The different methods were applied to various X-ray images of welds and the automatic inspection was evaluated and compared with visual inspection.

Proceedings of the Third International Conference on Computer Vision Theory and Applications, 2008

Radiographic inspection is a well-established testing method to detect weld defects. However, interpretation of radiographic films is a difficult task. The reliability of such interpretation and the expense of training suitable experts have allowed that the efforts being made towards automation in this field. In this paper, we describe an automatic detection system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features was proposed and extracted between defect candidates. In a third stage, an artificial neural network for weld defect classification was used under three regularisation process with different architectures. For the input layer, the principal component analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a different number of neurons was used in the aim to give better performance for defect classification in both cases.

2005

In non destructive testing by radiography, a perfect knowledge of the weld defect shape is an essential step to appreciate the quality of the weld and make decision on its acceptability or rejection. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of thresholding methods must be done judiciously. In this paper, performance criteria are used to conduct a comparative study of four non parametric histogram thresholding methods for automatic extraction of weld defect in radiographic images.