Deep Learning Based Steel Pipe Weld Defect Detection

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.

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Weld Defect Detection and Image Defect Recognition Using Deep Learning Technology

10.21203/rs.3.rs-149365/v1 ◽

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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Deep Learning Based Defect Detection for Solder Joints on Industrial X-Ray Circuit Board Images

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) ◽

10.1109/indin45582.2020.9442142 ◽

2020 ◽

Author(s):

Qianru Zhang ◽

Meng Zhang ◽

Chinthaka Gamanayake ◽

Chau Yuen ◽

Zehao Geng ◽

...

Keyword(s):

Deep Learning ◽

Defect Detection ◽

Solder Joints ◽

Circuit Board ◽

X Ray

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Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

Journal of Imaging ◽

10.3390/jimaging7030046 ◽

2021 ◽

Vol 7 (3) ◽

pp. 46

Author(s):

Jiajun Zhang ◽

Georgina Cosma ◽

Jason Watkins

Keyword(s):

Deep Learning ◽

Wind Turbine ◽

Turbine Blade ◽

Defect Detection ◽

Recognition Rate ◽

Weighted Average ◽

Wind Turbine Blade ◽

Evaluation Measures ◽

Augmentation Techniques ◽

The Mean

Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.

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Surface Defect Detection Methods Based on Deep Learning: a Brief Review

2020 2nd International Conference on Information Technology and Computer Application (ITCA) ◽

10.1109/itca52113.2020.00049 ◽

2020 ◽

Author(s):

Guanlin Liu

Keyword(s):

Deep Learning ◽

Defect Detection ◽

Surface Defect ◽

Detection Methods ◽

Surface Defect Detection

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

Sensors ◽

10.3390/s21134292 ◽

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.

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