Anisotropic diffusion based denoising on X-radiography images to detect weld defects

Malarvel, Muthukumaran; Sethumadhavan, Gopalakrishnan; Bhagi, Purna Chandra Rao; Kar, Soumitra; Thangavel, Saravanan; Krishnan, Arunmuthu

doi:10.1016/j.dsp.2017.05.014

Cited by 47 publications

(15 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some approaches are effective to aid the defect segmentation, like noise reduction [17] and contrast enhancement. And Qi et al [18] proposed a denoising approach based on independent component correlation algorithm (ICA).…”

Section: Methodsmentioning

confidence: 99%

Welding defect detection based on local image enhancement

Zhang

Dai

et al. 2019

IET image process

View full text Add to dashboard Cite

Welding defect detection in a radiographic image is an important topic in the field of industrial non‐destructive testing. To improve the accuracy of welding defect segmentation, a local image enhancement approach is proposed. In this algorithm, the requirement of contrast enhancement is considered when extracting the weld seam and segmenting the weld defect. The whole defect detection is conducted by three procedures: image enhancement, welding seam extraction, and defect segmentation. Firstly, a method for determining the Localised Pixel Inhomogeneity Factor (LPIF) is proposed. Then, based on the results of LPIF, the Otsu method is applied to segment the welding seam and defects are, identified by region growing algorithm. The authors compared LPIF with histogram equalisation, adaptive histogram equalisation, and contrast‐limited adaptive histogram equalisation algorithms and assessed its performance by using indicators such as image contrast, image definition, and edge intensity. Moreover, the authors compared the segmentation results of the enhanced defect images with the original image to further study the method's effect on weld defect segmentation. More than 70 images containing various types of defects are tested. The experimental results demonstrate that the quality of enhanced defect images is improved significantly, and has a high relative segmentation accuracy of more than 92%.

show abstract

Section: Methodsmentioning

confidence: 99%

Welding defect detection based on local image enhancement

Zhang

Dai

et al. 2019

IET image process

View full text Add to dashboard Cite

show abstract

“…This stage mainly follows the process from image pre-processing to feature extraction and then to feature selection and feature recognition. The image pre-processing stage mainly involves operations such as filtering (Feng et al 2020), denoising (Malarvel et al 2017), enhancement (Lin, Wu, and Hong 2012), and edge detection (Yu et al 2017). The primary geometric features in industrial images (Valavanis and Kosmopoulos 2010) or feature descriptors represented by SIFT (Yang et al 2020), HOG (Ming et al 2019), and LBP (Zhou et al 2020) are mainly extracted in the feature extraction stage.…”

Section: Current Development Of Iiprmentioning

confidence: 99%

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Liu

Bao

Wang

et al. 2021

International Journal of Computer Integrated Manufacturing

View full text Add to dashboard Cite

Deep learning (DL) is an important enabling technology for intelligent manufacturing. The DLbased industrial image pattern recognition (DLBIIPR) plays a vital role in the improvement of product quality and production efficiency. Although DL technology has been widely used in the field of natural image, industrial image often has some mixed characteristics, such as small sample, imbalance, small target, strong interference, fine-grained, temporality and semantical, which reduce the feasibility and generalization of DLBIIPR. To solve this problem, this paper provides an overview of approaches commonly used in industry by enriching the sample space and limiting the hypothesis space. In order to improve the confidence of front-line workers in using DL models, the explainable deep learning (XDL) methods are reviewed, and a case study is used to verify the effectiveness of XDL.

show abstract

“…This method c an extract some relatively obvious defects effectively to a certain extent, but the effect of extr acting defects of complex shape is not obvious. Malarvel et al proposed an improved anisotr opic diffusion model based on X-ray pipe weld images [21], which considers the adaptive thr eshold parameters in the local gray probability value and diffusion coefficient function to ach ieve the meaning of adjusting the low edge gradient in the noise image feature space. The per formance of the model was evaluated using indices such as mean square error, signal-to-noise ratio, and entropy, and the reliability of the model was verified.…”

Section: Related Workmentioning

confidence: 99%

Oil Pipeline Weld Defect Identification System Based on Convolutional Neural Network

2020

KSII TIIS

View full text Add to dashboard Cite

The automatic identification and classification of image-based weld defects is a difficult task due to the complex texture of the X-ray images of the weld defect. Several depth learning methods for automatically identifying welds were proposed and tested. In this work, four different depth convolutional neural networks were evaluated and compared on the 1631 image set. The concavity, undercut, bar defects, circular defects, unfused defects and incomplete penetration in the weld image 6 different types of defects are classified. Another contribution of this paper is to train a CNN model "RayNet" for the dataset from scratch. In the experiment part, the parameters of convolution operation are compared and analyzed, in which the experimental part performs a comparative analysis of various parameters in the convolution operation, compares the size of the input image, gives the classification results for each defect, and finally shows the partial feature map during feature extraction with the classification accuracy reaching 96.5%, which is 6.6% higher than the classification accuracy of other existing fine-tuned models, and even improves the classification accuracy compared with the traditional image processing methods, and also proves that the model trained from scratch also has a good performance on small-scale data sets. Our proposed method can assist the evaluators in classifying pipeline welding defects.

show abstract

Anisotropic diffusion based denoising on X-radiography images to detect weld defects

Cited by 47 publications

References 23 publications

Welding defect detection based on local image enhancement

Welding defect detection based on local image enhancement

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Oil Pipeline Weld Defect Identification System Based on Convolutional Neural Network

Contact Info

Product

Resources

About