An Non Destructive Test for the Detection of Weld Defects Using Image Processing

Kalaiselvi,; D, John Aravindhar

doi:10.11591/ijeecs.v9.i3.pp764-770

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…In 2018, Kalaiselvi and John Aravindhar [ 19 ] developed a computer-aided detection (CAD) system depending on image processing techniques to identify weld defects. Here, X-ray images are used in non-destructive testing.…”

Section: Literature Reviewmentioning

confidence: 99%

Significant effect of image contrast enhancement on weld defect detection

Mustafa,

Yazid,

Alquran

et al. 2024

PLoS ONE

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Weld defect inspection is an essential aspect of testing in industries field. From a human viewpoint, a manual inspection can make appropriate justification more difficult and lead to incorrect identification during weld defect detection. Weld defect inspection uses X-radiography testing, which is now mostly outdated. Recently, numerous researchers have utilized X-radiography digital images to inspect the defect. As a result, for error-free inspection, an autonomous weld detection and classification system are required. One of the most difficult issues in the field of image processing, particularly for enhancing image quality, is the issue of contrast variation and luminosity. Enhancement is carried out by adjusting the brightness of the dark or bright intensity to boost segmentation performance and image quality. To equalize contrast variation and luminosity, many different approaches have recently been put forth. In this research, a novel approach called Hybrid Statistical Enhancement (HSE), which is based on a direct strategy using statistical data, is proposed. The HSE method divided each pixel into three groups, the foreground, border, and problematic region, using the mean and standard deviation of a global and local neighborhood (luminosity and contrast). To illustrate the impact of the HSE method on the segmentation or detection stage, the datasets, specifically the weld defect image, were used. Bernsen and Otsu’s methods are the two segmentation techniques utilized. The findings from the objective and visual elements demonstrated that the HSE approach might automatically improve segmentation output while effectively enhancing contrast variation and normalizing luminosity. In comparison to the Homomorphic Filter (HF) and Difference of Gaussian (DoG) approaches, the segmentation results for HSE images had the lowest result according to Misclassification Error (ME). After being applied to the HSE images during the segmentation stage, every quantitative result showed an increase. For example, accuracy increased from 64.171 to 84.964. In summary, the application of the HSE method has resulted in an effective and efficient outcome for background correction as well as improving the quality of images.

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Section: Literature Reviewmentioning

confidence: 99%

Significant effect of image contrast enhancement on weld defect detection

Mustafa,

Yazid,

Alquran

et al. 2024

PLoS ONE

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show abstract

“…The initial dataset is optimised by adopting data augmentation methods such as rotational and translational transformation, image noise enhancement [3], etc. Improving the model's adaptive capability and detection performance requires increasing the variety and quantity of the dataset.…”

Section: Introductionmentioning

confidence: 99%

Research on the Improved YOLOv5s Tube Sheet Weld Defect Detection Method

Yu,

Wu,

Wang

et al. 2024

Advances in Transdisciplinary Engineering

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In the realm of machine learning-based target detection, there exist several challenges that require attention, namely limited detection range, complex feature extraction, suboptimal detection precision, and significant subjectivity. In this paper, the strengths and weaknesses of existing deep learning target detection algorithms have been investigated in order to address these issues by integrating the actual welding process of heat exchangers. The objective is to improve the model’s detection accuracy and speed. To achieve this, we employ the YOLOv5 model to detect and identify weld defects of the heat exchanger tube plate, and propose an enhancement method based on the YOLOv5s model. By implementing several enhancements, such as incorporating the attention mechanism, updating the loss function, and optimizing the feature fusion network, the model’s overall performance is enhanced, with a focus on addressing the issues of low detection accuracy, slow convergence, and inadequate real-time performance in detecting small target defects compared to the YOLOv5s model. The improved YOLOv5s_m model improves the detection accuracy by 4.52% and the speed by 4.4 FPS, which solves the problems of low detection accuracy, weak sensitivity of small target defect detection and poor convergence of the bounding box loss function of the YOLOv5s model. These improvements lay the groundwork for enhancing the automation and intelligence of weld quality inspections.

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Approach to weld segmentation and defect classification in radiographic images of pipe welds

Golodov

Maltseva

2022

NDT & E International

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An Non Destructive Test for the Detection of Weld Defects Using Image Processing

Cited by 5 publications

References 14 publications

Significant effect of image contrast enhancement on weld defect detection

Significant effect of image contrast enhancement on weld defect detection

Research on the Improved YOLOv5s Tube Sheet Weld Defect Detection Method

Approach to weld segmentation and defect classification in radiographic images of pipe welds

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