scholarly journals Review on Computer Aided Weld Defect Detection from Radiography Images

2020 ◽  
Vol 10 (5) ◽  
pp. 1878 ◽  
Author(s):  
Wenhui Hou ◽  
Dashan Zhang ◽  
Ye Wei ◽  
Jie Guo ◽  
Xiaolong Zhang
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Future Research ◽  
Defect Classification ◽  
Interest Point ◽  
Defect Inspection ◽  
Weld Joints ◽  
Weld Defect ◽  
Computer Aided ◽  
Human Interpretation

The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers.

scholarly journals Efficient Cell Segmentation from Electroluminescent Images of Single-Crystalline Silicon Photovoltaic Modules and Cell-Based Defect Identification Using Deep Learning with Pseudo-Colorization

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4292
Author(s):  
Horng-Horng Lin ◽  
Harshad Kumar Dandage ◽  
Keh-Moh Lin ◽  
You-Teh Lin ◽  
Yeou-Jiunn Chen
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Crystalline Silicon ◽  
Cell Segmentation ◽  
Solar Pv ◽  
Single Crystalline Silicon ◽  
Defect Inspection ◽  
Defect Identification ◽  
Pv Modules ◽  
A Cell

Solar cells may possess defects during the manufacturing process in photovoltaic (PV) industries. To precisely evaluate the effectiveness of solar PV modules, manufacturing defects are required to be identified. Conventional defect inspection in industries mainly depends on manual defect inspection by highly skilled inspectors, which may still give inconsistent, subjective identification results. In order to automatize the visual defect inspection process, an automatic cell segmentation technique and a convolutional neural network (CNN)-based defect detection system with pseudo-colorization of defects is designed in this paper. High-resolution Electroluminescence (EL) images of single-crystalline silicon (sc-Si) solar PV modules are used in our study for the detection of defects and their quality inspection. Firstly, an automatic cell segmentation methodology is developed to extract cells from an EL image. Secondly, defect detection can be actualized by CNN-based defect detector and can be visualized with pseudo-colors. We used contour tracing to accurately localize the panel region and a probabilistic Hough transform to identify gridlines and busbars on the extracted panel region for cell segmentation. A cell-based defect identification system was developed using state-of-the-art deep learning in CNNs. The detected defects are imposed with pseudo-colors for enhancing defect visualization using K-means clustering. Our automatic cell segmentation methodology can segment cells from an EL image in about 2.71 s. The average segmentation errors along the x-direction and y-direction are only 1.6 pixels and 1.4 pixels, respectively. The defect detection approach on segmented cells achieves 99.8% accuracy. Along with defect detection, the defect regions on a cell are furnished with pseudo-colors to enhance the visualization.

Image-based Surface Defect Detection Using Deep Learning: A Review

2021 ◽  
pp. 1-23
Author(s):  
Prahar Bhatt ◽  
Rishi K. Malhan ◽  
Pradeep Rajendran ◽  
Brual Shah ◽  
Shantanu Thakar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defects ◽  
Future Research ◽  
Specific Class ◽  
Survey Paper ◽  
Context Learning ◽  
Surface Defect Detection ◽  
Manufacturing Applications ◽  
Processing Techniques

Abstract Automatically detecting surface defects from images is an essential capability in manufacturing applications. Traditional image processing techniques were useful in solving a specific class of problems. However, these techniques were unable to handle noise, variations in lighting conditions, and background with complex textures. Increasingly deep learning is being explored to automate defect detection. This survey paper presents three different ways of classifying various efforts. These are based on defect detection context, learning techniques, and defect localization and classification method. The existing literature is classified using this methodology. The paper also identifies future research directions based on the trends in the deep learning area.

scholarly journals A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

2021 ◽  
Vol 11 (20) ◽  
pp. 9508
Author(s):  
Francisco López de la Rosa ◽  
Roberto Sánchez-Reolid ◽  
José L. Gómez-Sirvent ◽  
Rafael Morales ◽  
Antonio Fernández-Caballero
Keyword(s):  
Deep Learning ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Defect Detection ◽  
Scoping Review ◽  
Semiconductor Manufacturing ◽  
Manufacturing Industry ◽  
Future Research ◽  
Wide Range ◽  
Scanning Electron

Continued advances in machine learning (ML) and deep learning (DL) present new opportunities for use in a wide range of applications. One prominent application of these technologies is defect detection and classification in the manufacturing industry in order to minimise costs and ensure customer satisfaction. Specifically, this scoping review focuses on inspection operations in the semiconductor manufacturing industry where different ML and DL techniques and configurations have been used for defect detection and classification. Inspection operations have traditionally been carried out by specialised personnel in charge of visually judging the images obtained with a scanning electron microscope (SEM). This scoping review focuses on inspection operations in the semiconductor manufacturing industry where different ML and DL methods have been used to detect and classify defects in SEM images. We also include the performance results of the different techniques and configurations described in the articles found. A thorough comparison of these results will help us to find the best solutions for future research related to the subject.

Weld Defect Inspection of Battery Pack Based on Deep Learning of Linear Array Image

2020 ◽  
Vol 57 (22) ◽  
pp. 221502
Author(s):  
吴桐 Wu Tong ◽  
杨金成 Yang Jincheng ◽  
廖瑞颖 Liao Ruiying ◽  
杨凌辉 Yang Linghui
Keyword(s):  
Deep Learning ◽  
Linear Array ◽  
Battery Pack ◽  
Defect Inspection ◽  
Weld Defect

scholarly journals Deep Learning Based Steel Pipe Weld Defect Detection

2021 ◽  
pp. 1-13
Author(s):  
Dingming Yang ◽  
Yanrong Cui ◽  
Zeyu Yu ◽  
Hongqiang Yuan
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Steel Pipe ◽  
Weld Defect

scholarly journals Weld Defect Detection and Image Defect Recognition Using Deep Learning Technology

2021 ◽  
Author(s):  
Yu Cheng ◽  
HongGui Deng ◽  
YuXin Feng ◽  
JunJiang Xiang
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Defect Detection ◽  
Learning Algorithm ◽  
Image Features ◽  
Economic Losses ◽  
Weld Defects ◽  
Weld Defect ◽  
Defect Recognition ◽  
Image Defect

Abstract 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

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