Deep learning-based detection of aluminum casting defects and their types

Parlak, İsmail Enes; Emel, Erdal

doi:10.1016/j.engappai.2022.105636

Cited by 45 publications

(16 citation statements)

References 52 publications

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“…To automate this other piece of the puzzle, researchers have developed Automatic Defect Recognition (ADR) for NDT digital radiography, and these solutions aim to enhance the detection and evaluation of flaws in the acquired digital radiographs of manufactured components by using different deep-learning algorithms [17]. In recent years, the prevalence of ADR systems in NDT radiography has significantly increased, gaining recognition in the industry and research [18][19][20][21]. If adequately trained, ADR solutions based on deep-learning algorithms could assess radiographic images and automatically detect flaws, thereby increasing its potential to improve flaw detection accuracy, decrease the likelihood of human error in image evaluation, and increase image evaluation throughput.…”

Section: Automated Defect Recognition (Adr) In Digital Ndt Radiographymentioning

confidence: 99%

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study

Hena

Wei

Ibarra-Castanedo

et al. 2023

Sensors

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In response to the growing inspection demand exerted by process automation in component manufacturing, non-destructive testing (NDT) continues to explore automated approaches that utilize deep-learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep-learning models. This study investigated the influence of two image-quality parameters, namely signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), on the performance of a U-net deep-learning semantic segmentation model. Input images were acquired with varying combinations of exposure factors, such as kilovoltage, milli-ampere, and exposure time, which altered the resultant radiographic image quality. The data were sorted into five different datasets according to their measured SNR and CNR values. The deep-learning model was trained five distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection-over-union (IoU) metric of 0.9594 on test data of the same category but dropped to 0.5875 when tested on lower CNR test data. The result of this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters in order to enhance the performance of deep-learning segmentation models for NDT digital X-ray radiography applications.

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Section: Automated Defect Recognition (Adr) In Digital Ndt Radiographymentioning

confidence: 99%

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study

Hena

Wei

Ibarra-Castanedo

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…To automate this other piece of the puzzle, researchers have developed Automatic Defect Recognition (ADR) for NDT digital radiography, and these solutions aim to enhance the detection and evaluation of flaws in the acquired radiographs of manufactured components using different deep learning algorithms [18]. In recent years, the prevalence of ADR systems in NDT radiography has significantly increased, gaining recognition in the industry and research [19][20][21][22]. If adequately trained, ADR solutions based on deep learning algorithms could assess radiographic images and automatically detect flaws, thereby increasing its potential to improve flaw detection accuracy, decrease the likelihood of human error in image evaluation, and increase image evaluation throughput.…”

Section: Automated Defect Recognition (Adr) In Digital Ndt Radiographymentioning

confidence: 99%

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters - An Experimental Study

Hena¹,

Wei²,

Ibarra-Castanedo³

et al. 2023

Preprint

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In response to the growing inspection demand exerted by process automation in component manufacturing, Non-destructive testing (NDT) continues to explore automated approaches that utilize deep learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep learning models. This study investigates the influence of two image quality parameters, namely Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), on the performance of U-net deep learning segmentation model. Input images were acquired with varying combinations of exposure factors such as kilovoltage, milli-ampere, and exposure time, which altered the resultant quality. The data was sorted into 5 different datasets according to their measured SNR and CNR values. The deep learning model was trained 5 distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection over Union (IoU) metric of 0.9594 on test data of the same category but drops to 0.5875 when tested on lower CNR test data. The result in this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters, to enhance the performance of deep learning segmentation models in NDT radiography applications.

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“…By leveraging deep learning algorithms 6 , 7 , computer vision technology can effectively identify and localize target objects in images or videos. This technology has been widely applied in various detection tasks, such as industrial inspection 8 , face detection 9 , pedestrian detection 10 , vehicle detection 11 , medical detection 12 , and human action recognition 13 , achieving good results.…”

Section: Introductionmentioning

confidence: 99%

Post-secondary classroom teaching quality evaluation using small object detection model

Wang,

Chen,

Tian

et al. 2024

Sci Rep

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The classroom video has a complex background and dense targets. This study utilizes small object detection technology to analyze and evaluate students’ behavior in the classroom, aiming to objectively and accurately assess classroom quality. Firstly, noise is removed from the images using a median filter, and the contrast of the images is enhanced through histogram equalization. Label smoothing is applied to reduce the model’s sensitivity to labels. Then, features are extracted from the preprocessed images, and multi-scale feature fusion is employed to enhance semantic expression across multiple scales. Finally, a combination loss function is utilized to improve the accuracy of multi-object recognition tasks. Real-time detection of students’ behaviors in the classroom is performed based on the small object detection model. The average head-up rate in the classroom is calculated, and the quality of teaching is evaluated and analyzed. This study explores the methods and applications of small object detection technology based on actual teaching cases and analyzes and evaluates its effectiveness in evaluating the quality of higher education classroom teaching. The research findings demonstrate the significant importance of small object detection technology in effectively evaluating students’ learning conditions in higher education classrooms, leading to improved teaching quality and personalized education.

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Deep learning-based detection of aluminum casting defects and their types

Cited by 45 publications

References 52 publications

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters - An Experimental Study

Post-secondary classroom teaching quality evaluation using small object detection model

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