An autonomous technique for weld defects detection and classification using multi-class support vector machine in X-radiography image

Malarvel, Muthukumaran; Singh, Harjeet

doi:10.1016/j.ijleo.2021.166342

Cited by 50 publications

(19 citation statements)

References 22 publications

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“…Defect detection models based on pattern recognition have achieved many fruitful results, among which neural networks, Support Vector Machines (SVMs), Decision Trees, and Fuzzy Reasoning are ubiquitous. Malarvel and Singh (2021) trained a Multi-Layer Perceptron (MLP) to detect and recognize 60 weld defects using the known defect features in the collected original weld images, achieving an accuracy of 97.96% [34].…”

Section: Research Progress Of Weld Defect Image Recognitionmentioning

confidence: 99%

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

Deng

Feng

et al. 2021

Symmetry

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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.

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Section: Research Progress Of Weld Defect Image Recognitionmentioning

confidence: 99%

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

Deng

Feng

et al. 2021

Symmetry

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“…To compare the effectiveness of the Bayesian model for skin color recognition in this study, the comparison model uses artificial neural networks (ANN) [40] and SVM [41]. The experimental results are shown in Table 2:…”

Section: Skin Color Recognition Experimentmentioning

confidence: 99%

A Violation Information Recognition Method of Live‐Broadcasting Platform Based on Machine Learning Technology

Shen

Yuan

2021

Wireless Communications and Mobile Computing

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With the development of the live broadcast industry, security issues in the live broadcast process have become increasingly apparent. At present, the supervision of various live broadcast platforms is basically in a state of human supervision. Manpower supervision is mainly through user reporting and platform supervision measures. However, there are a large number of live broadcast rooms at the same time, and only relying on human supervision can no longer meet the monitoring needs of live broadcasts. Based on this situation, this study proposes a violation information recognition method of a live-broadcasting platform based on machine learning technology. By analyzing the similarities and differences between normal live broadcasts and violation live broadcasts, combined with the characteristics of violation image data, this study mainly detects human skin color and sensitive parts. A prominent feature of violation images is that they contain a large area of naked skin, and the ratio of the area of naked skin to the overall image area of the violation image will exceed the threshold. Skin color recognition plays a role in initial target positioning. The accuracy of skin color recognition is directly related to the recognition accuracy of the entire system, so skin color recognition is the most important part of violation information recognition. Although there are many effective skin color recognition technologies, the accuracy and stability of skin color recognition still need to be improved due to the influence of various external factors, such as light intensity, light source color, and physical equipment. When it is detected that the area of the skin color in the live screen exceeds the threshold, it is preliminarily determined to be a suspected violation video. In order to improve the recognition accuracy, it is necessary to detect sensitive parts of the suspected video. Naked female breasts are a very obvious feature in violation images. This study uses a chest feature extraction method to detect the chest in the image. When the recognition result is a violation image, it is determined that the live broadcast involves violation content. The machine learning algorithm is simple to implement, and the parameters are easy to adjust. The classifier training requires a short time and is suitable for live violation information recognition scenarios. The experimental results on the adopted data set show that the method used in this article can effectively detect videos with violation content. The recognition rate is as high as 85.98%, which is suitable for a real-life environment and has good practical significance.

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“…Choi and Seo [10] used deep learning techniques to identify cracks, scratches, and other irregular flaws often observed in industrial parts. To identify irregularly shaped defects arising from processes such as welding, researchers used machine learning and image filtering technology [11]. Image-based deep learning techniques, however, result in a loss of pixel location data due to the inherent flaws in the convolutional neural network (CNN) structure, as well as flaws in the technology in identifying characteristics and patterns from the learned data to independently classify the results.…”

Section: Introductionmentioning

confidence: 99%

Automatic Screening of Bolts with Anti-Loosening Coating Using Grad-CAM and Transfer Learning with Deep Convolutional Neural Networks

Noh

Hong

2022

Applied Sciences

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Most electronic and automotive parts are affixed by bolts. To prevent such bolts from loosening through shock and vibration, anti-loosening coating is applied to their threads. However, during the coating process, various defects can occur. Consequently, as the quality of the anti-loosening coating is critical for the fastening force, bolts are inspected optically and manually. It is difficult, however, to accurately screen coating defects owing to their various shapes and sizes. In this study, we applied deep learning to assess the coating quality of bolts with anti-loosening coating. From the various convolutional neural network (CNN) methods, the VGG16 structure was employed. Furthermore, the gradient-weighted class activation mapping visualization method was used to evaluate the training model; this is because a CNN cannot determine the classification criteria or the defect location, owing to its structure. The results confirmed that external factors influence the classification. We, therefore, applied the region of interest method to classify the bolt thread only, and subsequently, retrained the algorithm. Moreover, to reduce the learning time and improve the model performance, transfer learning and fine tuning were employed. The proposed method for screening coating defects was applied to a screening device equipped with an actual conveyor belt, and the Modbus TCP protocol was used to transmit signals between a programmable logic controller and a personal computer. Using the proposed method, we were able to automatically detect coating defects that were missed by optical sorters.

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An autonomous technique for weld defects detection and classification using multi-class support vector machine in X-radiography image

Cited by 50 publications

References 22 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

A Violation Information Recognition Method of Live‐Broadcasting Platform Based on Machine Learning Technology

Automatic Screening of Bolts with Anti-Loosening Coating Using Grad-CAM and Transfer Learning with Deep Convolutional Neural Networks

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