Accurate segmentation of weld defects with horizontal shapes

Radi, Doaa; Abo-Elsoud, M.A.; Khalifa, Fahmi

doi:10.1016/j.ndteint.2021.102599

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…Subsequently, formulas (8)- (11) were used to calculate the mean µ t , variance σ 2 t , weight w t and bias b t of each neuron. Add these values into formula (7) to obtain the minimum energy functione * t , as shown in formula ( 12) where…”

Section: Simam Attention Modulementioning

confidence: 99%

“…Finally, a density based noise application spatial clustering considering gray constraint is designed to identify real defects from the candidate defect feature point set. Radi et al [7] proposed a novel image processing flow for the segmentation of welding defects. It used two new filters to separate the background and defect features, and added a separate convolution process for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

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Small-modulus worms surface defect detection method based on YOLOv7

Li,

Zheng,

et al. 2024

Meas. Sci. Technol.

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Small-modulus worms are widely used in precision transmission mechanisms. The detection of surface defects on processed Small-modulus worms mainly relies on manual inspection. However, this detection method has low detection efficiency and low accuracy. In response to this issue, this paper proposed a surface defect detection method for small-modulus worms based on deep learning. Firstly, based on the geometric features and material properties of small-modulus worms, designed a two-cameras worms image acquisition system to capture small-modulus worm images. And the captured images were annotated and classified to construct a small-modulus worms defect dataset. Secondly, the YOLOv7 network model algorithm was studied and improved. A three-stage image preprocessing algorithm was added to the YOLOV7 model to reduce the interference in distorted areas. Next, Ghost-CBS structures were employed to replace some of the conventional CBS structures, reducing computational complexity. And we also added a width-height balance weights and alpha structure to the eiou loss function. Improve the fitting performance of the model on the bounding box with large aspect ratio. Furthermore, three SimAM attention modules were added to the backbone, increasing the network's focus on key areas. Finally, experimental validation was conducted, and the results demonstrated that the performance of our proposed method is better than other existing methods. The detection accuracy reached 95.7%, with a map@50 of 94.6%. Overall, the performance met the requirements for worm defect detection.

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Section: Simam Attention Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Small-modulus worms surface defect detection method based on YOLOv7

Li,

Zheng,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…However, it is worth noting that directly compressing the high bits data to 8 bits data will inevitably lose a lot of grey scale information. In this paper, to solve the problem of information loss, a new preprocessing model is designed to introduce the LOG function transformation, as shown in the calculation formula (1). The formula can extend the 4096 levels data to the 36124 levels.…”

Section: Raw-optical Mapping Modelmentioning

confidence: 99%

“…With the development of computer technology, the traditional method of X-ray film inspection using searchlights is gradually eliminated, while the industrial X-ray films captured by existing line array cameras are 12-bit high dynamic range images, which cannot be displayed properly on existing monitors. Secondly, to extract high-quality industrial radiographic films for weld port number, weld date identification, and defect detection [1] [2], corresponding enhancement is required to improve image clarity for discriminating image information [3] [4].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Enhancement of High Greyscale Weld Images Based on the New Pixel Self-transformation Method

Ren

et al. 2023

J. Phys.: Conf. Ser.

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X-ray nondestructive testing technology is widely used in the weld detection of oil and gas pipeline industry. The industrial welding manufacturing field produces 12-bit and higher high grey scale images that cannot be displayed directly on an 8-bit display. In this paper, we propose an adaptive pixel self-transformation method for high greyscale weld images. The method takes into account the characteristics of weld images and human perception. Firstly, a high dynamic range tone mapping model is designed, which converts the original RAW data into optical observable data. Secondly, in order to solve the problem that the traditional pixel self-transformation method can only adapt to the input with 256 levels of greyscale and cannot process the high-bit image data. An adaptive high-bit pixel self-transformation is proposed. Finally, the method of this paper was tested on 12-bit X-ray films. The experimental results show that the designed method, which improves the recognizability of weld images and can effectively obtain weld images with outstanding details, is an efficient weld feature enhancement algorithm.

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“…Radi et al [14] propose the segmentation techniques for weld defects with horizontal shapes. Proposed method compared to traditional approaches for detection of weld defects and achieved high segmentation accuracy.…”

Section: A Image Processing Techniquesmentioning

confidence: 99%

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

et al. 2022

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The final stage of the production process in the industry is quality control. Quality control answers the question of is there a defect on the surface of the products. Frequently the quality control is performed manually. The disadvantages of manual quality control are high error rate (low accuracy), low product rate (low performance) and high expense rate (high cost). The solution is automatic quality control using machine vision systems. These systems classify the products and segment the defects on their surfaces by processing the images taken by cameras during the production process in real-time. Some products like military cartridge cases have metallic, cylindrical, non-uniform texture and highly reflective surface. So, the quality of images is very important. Another factor that affects the accuracy is the nonuniform texture of the product surface. Distinguishing the product non-uniform texture from defect texture is a challenging problem. In previous works, this problem have been tried to be solved with image processing and deep learning techniques and the accuracy of 97% and 96% have been obtained, appropriately. According to NATO standards, the accuracy of the classification of the military cartridge cases should be above 99%. In this work, the methodology for classification of the military cartridge cases and segmentation of the defects on their surfaces with non-uniform texture is proposed to increase the accuracy. In scope of the proposed methodology the datasets with non-defective, defective and labeled/masked image classes of the cartridge cases were created, the deep learning models to classify the military cartridge cases and segment the defects on their surfaces were proposed, implemented and obtained results were evaluated using the metrics such as Accuracy, Precision, Recall, F1-Score, Jaccard Index (JI) and Mean Intersection over Union (mIoU). Obtained results showed that the proposed methodology increased the accuracy of classification to 100% with the DenseNet169 model and the F1-Score of segmentation to 92.1% with Improved U-Net and ResUnet models.

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Accurate segmentation of weld defects with horizontal shapes

Cited by 14 publications

References 22 publications

Small-modulus worms surface defect detection method based on YOLOv7

Small-modulus worms surface defect detection method based on YOLOv7

Adaptive Enhancement of High Greyscale Weld Images Based on the New Pixel Self-transformation Method

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

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