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automated defect detection From Ultrasonic Images Using Deep Learning

IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2021年第10期68卷 3126-3134页

作者： Medak, Duje Posilovic, Luka Subasic, Marko Budimir, Marko Loncaric, Sven Univ Zagreb Fac Elect Engn & Comp Zagreb 10000 Croatia INETEC Ltd Zagreb 10000 Croatia

Nondestructive evaluation (NDE) is a set of techniques used for material inspection and defect detection without causing damage to the inspected component. One of the commonly used nondestructive techniques is called ultrasonic inspection. The acquisition of ultrasonic data was mostly automated in recent years, but the analysis of the collected data is still performed manually. This process is thus very expensive, inconsistent, and prone to human errors. An automated system would significantly increase the efficiency of analysis, but the methods presented so far fail to generalize well on new cases and are not used in real-life inspection. Many of the similar data analysis problems were recently tackled by deep learning methods. This approach outperforms classical methods but requires lots of training data, which is difficult to obtain in the NDE domain. In this work, we train a deep learning architecture EfficientDet to automatically detect defects from ultrasonic images. We showed how some of the hyperparameters can be tweaked in order to improve the detection of defects with extreme aspect ratios that are common in ultrasonic images. The proposed object detector was trained on the largest dataset of ultrasonic images that was so far seen in the literature. In order to collect the dataset, six steel blocks containing 68 defects were scanned with a phased-array probe. More than 4000 VC-B-scans were acquired and used for training and evaluation of EfficientDet. The proposed model achieved 89.6% of mean average precision (mAP) during fivefold cross validation, which is a significant improvement compared to some similar methods that were previously used for this task. A detailed performance overview for each of the folds revealed that EfficientDet-D0 successfully detects all of the defects present in the inspected material.

关键词： Acoustics Deep learning Detectors Probes Training Inspection Wavelet transforms automated defect detection deep learning flaw detection ultrasonic image analysis ultrasonic testing (UT)

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automated defect detection for sewer pipeline inspection and condition assessment

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AUTOMATION IN CONSTRUCTION 2009年第5期18卷 587-596页

作者： Guo, W. Soibelman, L. Garrett, J. H., Jr. Carnegie Mellon Univ Dept Civil & Environm Engn Pittsburgh PA 15213 USA

To regularly and proactively assess conditions of sewer infrastructure systems to ensure their structural integrity and continuity of services, it is critical to advance the state of automated pipeline inspection and condition assessment. Currently, a critical issue is to address realistic defect detection that deals with real sewer inspection data. This paper presents the findings of a research project that seeks to enable automated detection of defects in sewer pipelines from inspection videos and images. The need for and the challenges of automated defect detection in sewer infrastructure condition monitoring are presented. Based on a general detection and recognition model established in this paper, a change detection based approach which is tailored to solve the challenges in this sewer pipeline domain is described and illustrated through case study. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Sewer pipeline Inspection Condition assessment automated defect detection Image processing Pattern recognition

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automated defect detection for fast evaluation of real inline CT scans

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NONDESTRUCTIVE TESTING AND EVALUATION 2020年第3期35卷 266-275页

作者： Schlotterbeck, Maxim Schulte, Lukas Alkhaldi, Weaam Krenkel, Martin Toeppe, Eno Tschechne, Stephan Wojek, Christian Otto von Guericke Univ Inst Mat & Joining Technol Magdeburg Germany Carl Zeiss Ind Messtech GmbH Dept Applicat Dev Global Res & Dev Xray Oberkochen Germany

In this paper, we present a fully automatic evaluation approach that can be used for fast inline CT scanning. In contrast to classical defect-recognition algorithms, this approach does not require good image quality. Instead, it allows to distinguish CT artifacts from real defects introduced in the production process. To this end, a three-step workflow was developed, in which any deviations from a reference part are detected and subsequently classified and segmented, to allow automatic decision whether the part fulfills given quality requirements. We demonstrate this approach using CT scans of aluminum castings, typically used in the automotive industry. Also, a comparison between two different segmentation algorithms is shown.

关键词： Inline computed tomography automated defect detection machine learning

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automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine

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AUTOMATION IN CONSTRUCTION 2014年 38卷 1-13页

作者： Halfawy, Mahmoud R. Hengmeechai, Jantira Infrastruct Data Solut Inc IDS Regina SK S4S 7H9 Canada

Condition assessment of municipal sewer pipes using closed circuit television (CCTV) inspections is known to be time consuming, costly, and prone to errors primarily due to operator fatigue or novicity. automated detection of defects can provide a valuable tool for ensuring the quality, accuracy, and consistency of condition data, while reducing the time and cost of the inspection process. This paper presents an efficient pattern recognition algorithm to support automated detection and classification of pipe defects in images obtained from conventional CCTV inspection videos. The algorithm employs the histograms of oriented gradients (HOG) and support vector machine (SVM) to identify pipe defects. The algorithm involves two main steps: (1) image segmentation to extract suspicious regions of interest (ROI) that represent candidate defect areas;and (2) classification of the ROI using SVM classifier that was trained using sets of HOG features extracted from positive and negative examples of the defect. Proposed algorithm is applied to the problem of detecting tree root intrusions. The performance of linear and radial basis function SVM classifiers evaluated. The algorithm was tested on a set of actual CCTV videos obtained from the cities of Regina and Calgary in Canada. Experimental results demonstrated the viability and robustness of the algorithm. (c) 2013 Published by Elsevier B.V.

关键词： automated defect detection Closed circuit television inspection Pipe defects Computer vision Pattern recognition Histograms of oriented gradients Machine learning Support vector machine

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automated defect detection for Mass-Produced Electronic Components Based on YOLO Object detection Models

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IEEE SENSORS JOURNAL 2024年第16期24卷 26877-26888页

作者： Mao, Wei-Lung Wang, Chun-Chi Chou, Po-Heng Liu, Yen-Ting Natl Yunlin Univ Sci & Technol NYUST Dept Elect Engn Touliu 64002 Yunlin Taiwan Natl Yunlin Univ Sci & Technol NYUST Grad Sch Engn Sci & Technol Touliu 64002 Yunlin Taiwan Acad Sinica AS Res Ctr Informat Technol Innovat CITI Taipei 11529 Taiwan Natl Sun Yat Sen Univ NSYSU Inst Commun Engn ICE Kaohsiung 80424 Taiwan

Since the defect detection of conventional industry components is time-consuming and labor-intensive, it leads to a significant burden on quality inspection personnel and difficult to manage product quality. In this article, we propose an automated defect detection system for the dual in-line package (DIP) that is widely used in industry, using digital camera optics and deep learning (DL)-based model. The two most common defect categories of DIP are examined: 1) surface defects and 2) pin-leg defects. However, the lack of defective component images leads to a challenge for detection tasks. To solve this problem, the ConSinGAN is used to generate a suitable size dataset for training and testing. Four varieties of the you only look once (YOLO) model are investigated (v3, v4, v7, and v9), both in isolation and with the ConSinGAN augmentation. The proposed YOLOv7 with ConSinGAN is superior to the other YOLO versions in accuracy of 95.50%, detection time of 285 ms, and is far superior to threshold-based approaches. In addition, the supervisory control and data acquisition (SCADA) system is developed, and the associated sensor architecture is described. The proposed automated defect detection can be easily established with numerous types of defects or insufficient defect data.

关键词： YOLO Electronics packaging Cameras defect detection Sensors Surface waves Production automated defect detection automated optical inspection (AOI) camera sensor ConSinGAN deep learning (DL) dual in-line package (DIP) switch you only look once (YOLO)

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Enhancing solar photovoltaic modules quality assurance through convolutional neural network-aided automated defect detection

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RENEWABLE ENERGY 2023年第Part1期219卷

作者： Hassan, Sharmarke Dhimish, Mahmoud Univ York Sch Phys Engn & Technol Lab Photovolta York YO10 5DD England

Detecting cracks in solar photovoltaic (PV) modules plays an important role in ensuring their performance and reliability. The development of convolutional neural networks (CNNs) has introduced a game-changing dimension in the detection of defects in PV modules. This paper proposes an automated defect detection method for PV, by leveraging custom-designed CNN to accurately analyse electroluminescence (EL) images, identifying defects such as cracks, mini-cracks, potential induced degradation (PID), and shaded areas. The proposed system achieves a high level of validation accuracy of 98.07%, reducing manual inspection demands, enhancing quality standards, and saving costs. The system was validated in a case study for PV installations faulty with PID, where it identified all defective modules with a high degree of precision of 96.6%, surpassing existing methods. This methodology holds promise for revolutionizing PV industry quality control, improving module reliability, and supporting sustainable solar energy growth.

关键词： Convolutional neural network Artificial energy Photovoltaics automated defect detection Electroluminescence imaging

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automated Melt Pool Characterization for defect detection in Additive Manufacturing

Automated Melt Pool Characterization for Defect Detection in...

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Conference on Applications of Machine Learning

作者： Mantell, Sarah P. Wachtor, Adam J. Flynn, Garrison S. Ryder, Matthew A. Lados, Diana A. Los Alamos Natl Lab POB 1663Ms T001 Los Alamos NM 87545 USA Worcester Polytech Inst Integrat Mat Design Ctr 100 Inst Rd Worcester MA 01609 USA

ISBN: (纸本)9781510654389;9781510654396

Additive manufacturing (AM) is rapidly expanding as a result of its ability to efficiently produce complex parts that are difficult or impossible to fabricate with traditional manufacturing processes. However, a key factor currently limiting wide-scale implementation of this technology is the lack of confidence in the reliability of AM components. Laser powder bed fusion (LPBF) is an AM process where lasers are used to successively melt layers of metal powder to produce threedimensional parts. Selecting the appropriate processing parameters, such as laser power and scan speed, directly impacts the quality and performance of AM depositions. By studying the characteristics of single-track melt pools produced via LPBF, the appropriate processing parameters for a given build may be determined. Quantification of critical melt pool characteristics -such as width, depth, and surface morphology -is primarily performed through manual measurements on high resolution images;a process which can be immensely time consuming and is subject to user bias. In this study, computer vision techniques, such as edge detection and texture segmentation, are used to automatically detect and quantify these characteristics. Clustering methods were then applied to these measurements to classify the processing regime for the melt pool geometry and associated operating parameters used in its creation. Operators can then use the classification information to select build parameters which reduce the likelihood of defects in constructed parts. This characterization process has been demonstrated through automated classification of single-tracks experiments for 4340 steel produced by an EOS M290 LPBF system.

关键词： additive manufacturing automated defect detection laser powder bed fusion melt pools texture segmentation

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SPDP-Net: A Semantic Prior Guided defect Perception Network for automated Aero-Engine Blades Surface Visual Inspection

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 2724-2733页

作者： Ma, Yunfeng Liu, Min Zhang, Yiqiong Wang, Xueping Wang, Yaonan Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Natl Engn Res Ctr Robot Visual Percept & Control Changsha 410082 Hunan Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China

automated surface defect detection is essential to manufacturing automation. However, automated inspection of aero-engine blades remains challenging due to tiny defects and weak features. To address this issue, we propose a semantic prior guided defect perception network, named SPDP-Net, which is ultimately integrated into an automated system to achieve efficient detection of defects. Firstly, a semantic prior mining module (SPM) is developed to capture finer-grained pixel-level location priors of defects by leveraging image feature mapping relations which facilitates the precise perception of tiny defects. Subsequently, we propose a defect enhancement perception module (DEP) to separate weak defects from complex backgrounds by utilizing defect location priors provided by SPM to enhance the features of defects while suppressing the values of non-defect regions, which makes the weak defects present as more obvious outliers. Finally, the global information extraction module (GIE) extracts the global features of defects, which helps to further improve the predicted results. When equipped with SPM, DEP and GIE, SPDP-Net can accurately identify and locate defects, exhibiting more competitive recognition and feature extraction capabilities for tiny defects and weak defects. To evaluate the effectiveness of our method, we construct an aero-engine blade surface defect detection dataset from real industrial scenarios called ABSDD with the collaboration of senior engineers. We achieve 95.9% precision, 94.0% recall and 94.9% F1 score on ABSDD. In addition, we also achieve state-of-the-art results on two public benchmark datasets, KSDD2 and DAGM. Finally, we have applied the developed SPDP-Net to an automated system and have conducted actual tests in collaboration with a well-known aero-engine production company. Note to Practitioners-At present, the automated detection system for surface defects in industrial manufacturing has not been well developed, which is particularly tra

关键词： Manufacturing automation automated defect detection convolutional neural network (CNN) automated defect detection system

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Binary Neural Network for automated Visual Surface defect detection

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SENSORS 2021年第20期21卷 6868-6868页

作者： Liu, Wenzhe Zhang, Jiehua Su, Zhuo Zhou, Zhongzhu Liu, Li Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu 90570 Finland Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

As is well-known, defects precisely affect the lives and functions of the machines in which they occur, and even cause potentially catastrophic casualties. Therefore, quality assessment before mounting is an indispensable requirement for factories. Apart from the recognition accuracy, current networks suffer from excessive computing complexity, making it of great difficulty to deploy in the manufacturing process. To address these issues, this paper introduces binary networks into the area of surface defect detection for the first time, for the reason that binary networks prohibitively constrain weight and activation to +1 and -1. The proposed Bi-ShuffleNet and U-BiNet utilize binary convolution layers and activations in low bitwidth, in order to reach comparable performances while incurring much less computational cost. Extensive experiments are conducted on real-life NEU and Magnetic Tile datasets, revealing the least OPs required and little accuracy decline. When classifying the defects, Bi-ShuffleNet yields comparable results to counterpart networks, with at least 2x inference complexity reduction. defect segmentation results indicate similar observations. Some network design rules in defect detection and binary networks are also summarized in this paper.

关键词： automated defect detection binary network binary neural network efficient network automated visual inspection surface defect detection

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Tools and Process of defect detection in automated Manufacturing Systems

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第6期10卷

作者： Al-Jubori, Hussein N. Al-Darraji, Izzat Univ Baghdad Al Khwarizmi Coll Engn Automated Mfg Dept Baghdad 10070 Iraq

INTRODUCTION: A range of tools and technologies are at disposal for the purpose of defect detection. These include but are not limited to sensors, Statistical Process Control (SPC) software, Artificial Intelligence (AI) and machine learning (ML) algorithms, X-ray systems, ultrasound systems, and eddy current systems. OBJECTIVES: The determination of the suitable instrument or combination of instruments is contingent upon the precise production procedure and the category of flaw being identified. In certain cases, defects may necessitate real-time monitoring and analysis through the use of sensors and SPC software, whereas more comprehensive analysis may be required for other defects through the utilization of X-ray or ultrasound systems. METHODS: The utilization of AI and ML algorithms has gained significant traction in the realm of defect detection. This is attributed to their ability to process vast amounts of data and discern patterns that may have otherwise eluded detection. The aforementioned tools have the capability to anticipate potential flaws and implement pre-emptive measures to avert their occurrence. RESULTS: The detection of defects in automated manufacturing systems is a continuous process that necessitates meticulous observation and examination to guarantee prompt and effective identification and resolution of defects. CONCLUSION: The utilization of suitable tools and technologies is imperative for manufacturers to guarantee optimal production quality and operational success.

关键词： automated defect detection Artificial intelligence Machine vision Sensors Manufacturing systems

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