A Cumulative Sums Algorithm for Segmentation of Digital X-ray Images

Vorobeychikov, Sergey E.; Чахлов, С. В.; Udod, V. A.

doi:10.1007/s10921-019-0616-3

Cited by 2 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…X-ray weld detection is divided into radiographic film detection and digital detection according to the images. The former is highly subjective and prone to misjudgments [6], while the latter utilizes computer algorithms for image recognition and detection [7]. Machine learning and deep learning have been widely used in many fields.…”

Section: Introductionmentioning

confidence: 99%

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Deng

Feng

et al. 2021

Symmetry

View full text Add to dashboard Cite

Aiming at the problem of the poor robustness of existing methods to deal with diverse industrial weld image data, we collected a series of asymmetric laser weld images in the largest laser equipment workshop in Asia, and studied these data based on an industrial image processing algorithm and deep learning algorithm. The median filter was used to remove the noises in weld images. The image enhancement technique was adopted to increase the image contrast in different areas. The deep convolutional neural network (CNN) was employed for feature extraction; the activation function and the adaptive pooling approach were improved. Transfer Learning (TL) was introduced for defect detection and image classification on the dataset. Finally, a deep learning-based model was constructed for weld defect detection and image recognition. Specific instance datasets verified the model’s performance. The results demonstrate that this model can accurately identify weld defects and eliminate the complexity of manually extracting features, reaching a recognition accuracy of 98.75%. Hence, the reliability and automation of detection and recognition are improved significantly. The research results can provide a theoretical and practical reference for the defect detection of sheet metal laser welding and the development of the industrial laser manufacturing industry.

show abstract

Section: Introductionmentioning

confidence: 99%

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Deng

Feng

et al. 2021

Symmetry

View full text Add to dashboard Cite

show abstract

“…X-ray weld detection is divided into radiographic film detection and digital detection according to the images. The former is highly subjective and prone to misjudgments [9], while the latter utilizes computer algorithms for image recognition and detection [10]. The weld defect detection methods have been researched extensively.…”

Section: Introductionmentioning

confidence: 99%

Weld Defect Detection and Image Defect Recognition Using Deep Learning Technology

Deng

Feng

et al. 2021

Preprint

View full text Add to dashboard Cite

Welding defects not only bring several economic losses to enterprises and individuals but also threatens peoples lives. We propose a deep learning model, where the data-trained deep learning algorithm is employed to detect the weld defects, and the Convolutional Neural Networks (CNNs) are utilized to recognize the image features. The Transfer Learning (TL) is adopted to reduce the training time via simple adjustments and hyperparameter regulations. The designed deep learning-based model is compared with other classic models to prove its effectiveness in weld defect detection and image recognition further. The results show this model can accurately identify weld defects and eliminates the complexity of manually extracting features, reaching a recognition accuracy of 92.54%. Hence, the reliability and automation of detection and recognition is improved signifificantly. Actual application also verififies the effectiveness of TL in weld defect detection and image defect recognition. Therefore, our research results can provide theoretical and practical references for effificient automatic detection of steel plates, cost reduction, and the high-quality development of iron and steel enterprises.Index Terms - convolutional neural network, deep learning, image detect recognition, transfer learning, weld defect detection

show abstract

A Cumulative Sums Algorithm for Segmentation of Digital X-ray Images

Cited by 2 publications

References 15 publications

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Weld Defect Detection and Image Defect Recognition Using Deep Learning Technology

Contact Info

Product

Resources

About