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automated visual inspection and machine vision iv 2021

ISBN: (纸本)9781510644083

The proceedings contain 21 papers. The topics discussed include: irritation-free optical 3D-based measurement of tidal volume;a GPU-based ray tracing approach for the prediction of multireflections on measurement objects and the a priori estimation of low-reflection measurement poses;experimental setup for fast vis-NIR spectroscopy measurements in deflagrations;classification of municipal solid waste using deep convolutional neural network model applied to multispectral images;qualification of holistic and generic camera-system calibration by fringe projection;vision threads: a novel approach to generic camera calibration;and effective deep learning training method using blur and noise filter to detect defect in TFT-LCD PAD.

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machine vision System for Quality Control of Stents Used in Angioplasty 1

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8th International Scientific-Technical Conference (MANUFACTURING)

作者： Kujawinska, Agnieszka Diering, Magdalena Przybyl, Anna Poznan Univ Tech Pl M Sklodowskiej Curie 5 PL-60965 Poznan Poland

ISBN: (数字)9783031564673

ISBN: (纸本)9783031564697;9783031564673

Traditional visual inspection - compared to automated inspection is not very effective, but for various reasons it is still used in many industries, including medical one. This paper discusses an example of visual inspection in the manufacture of stents used in angioplasty. The use of such inspection is economically justified here due to the low probability of non-conformity, even with the associated high risk of error, which can result in loss of patient health. This paper presents the concept of automating stent quality assessment through the implementation of a machine vision System. To support operator decision making in the proposed system, a decision model using artificial neural networks has been developed. The paper demonstrates the potential of such an implementation to minimize multiple sources of weakness in the visual inspection used so far. The paper presents the verification results and evaluation of the feasibility of using a vision-based system for quality control of angioplasty stents.

关键词： machine vision System machine Learning Quality Control visual inspection Angioplasty Drug Eluting Stents

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Design and Implementation of AI machine vision inspection System with Multi-Optical Images for OLED Film Manufacturing 8

Design and Implementation of AI Machine Vision Inspection Sy...

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Kim, Tae-Yong Oh, Seongjin Lee, So-Min Lee, Sungkook Hwang, Sangyoon Jeong, Jongpil Sungkyunkwan University Dept. of Smart Factory Convergence Suwon Korea Republic of Sungkyunkwan University Dept. of Culture and Technology Suwon Korea Republic of Sungkyunkwan University Dept. of Mechanical Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Chemical Engineering Suwon Korea Republic of

ISBN: (纸本)9798350373615

machine vision technology is one of the main areas of rapid development in the modern world and is becoming increasingly important in various industries. This innovative technology uses the fusion of computer vision and artificial intelligence to process and analyze visual information, enabling the development of automated inspection systems and the automation of processes. In particular, machine vision technology is playing an increasingly important role in OLED film production and quality control. Currently, OLED film production is mainly based on visual inspection, but this method has various problems. It is labor-intensive and relies on humans, which limits the accuracy and consistency of the inspection. It is also difficult to identify defective elements with irregular shapes or sizes. To overcome these issues and revolutionize the field of OLED film production and quality control, we propose an OLED Film inspection Architecture with machine vision. This architecture utilizes advanced cameras and sensors to analyze the surface of OLED films in detail and automatically identify defective elements. In addition, by integrating multiple optical systems and AI deep learning algorithms, it can perform accurate inspections under various lighting conditions and detect defects in real time. By utilizing these advanced technologies, quality control and production efficiency can be greatly improved, which is expected to drive innovation in OLED film production. This research provides a high level of technical content and provides a bright outlook for the future of machine vision technology in the industrial field. © 2023 IEEE.

关键词： inspection

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Explainability of deep learning classifier decisions for optical detection of manufacturing defects in the automated Fiber Placement process 4

Explainability of deep learning classifier decisions for opt...

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Conference on automated visual inspection and machine vision iv

作者： Meister, Sebastian Wermes, Mahdieu A. M. Stueve, Jan Groves, Roger M. German Aerosp Ctr Ctr Lightweight Prod Technol Ottenbecker Damm 12 Stade Germany Delft Univ Technol Aerosp Nondestruct Testing Lab Kluyverweg 1 NL-2629 HS Delft Netherlands

ISBN: (数字)9781510644090

ISBN: (纸本)9781510644090

automated fibre layup techniques are commonly used composite manufacturing processes in the aviation sector and require a manual visual inspection. Neural Network classification of defects has the potential to automate this visual inspection, however, the machine decision-making processes are hard to verify. Thus, we present an approach for visualising Convolutional Neural Network (CNN) based classifications of manufacturing defects and quantifying its robustness. Our investigations have shown that especially Smoothed Integrated Gradients and DeepSHAP are particularly well suited for the visualisation of CNN classifications. The Smoothed Integrated Gradients technique also reveals advantages in robustness when evaluating degraded input images.

关键词： Defect classifications CNN Inline inspection xAI Computer vision Composite Manufacturing Laser Line Scan Sensor

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automated vision Systems for Condition Assessment of Sewer and Water Pipelines

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2021年第4期18卷 1861-1878页

作者： Rayhana, Rakiba Jiao, Yutong Zaji, Amirhossein Liu, Zheng Univ British Columbia Sch Engn Kelowna BC V1V 1V7 Canada

Sewer networks and water distribution systems are among the most valuable and critical urban assets for a community. These systems have their respective time span to provide continuous service to the residents. Hence, it is paramount to assess the condition of sewer and water pipelines to ensure sustainable, reliable, and cost-effective transportation of sewerage and water supply. The assessment is usually done by the inspection robots, which are equipped with machine vision systems and/or sensors. The inspection robots acquire the inspection data, and the operators then conduct the survey of the captured video and/or sensory data to interpret the results. Nowadays, automated solutions are being adopted by industry to achieve an accurate and efficient assessment. This article surveys the state of the art of the automated vision systems, which are employed for condition assessment of sewer and water pipelines, and identifies the challenges for future research. The following areas are highlighted in this survey: 1) the typical types of the faults and failures, which include the concept and definition of the sewer and water pipelines;2) the inspection systems, e.g., robotic platforms for sewer and water pipeline inspection;3) the machine vision systems for fault detection;4) the computational frameworks for condition assessment;and 5) the challenges and suggestions for future research. This article summarizes the current state of automation in the machine vision technology for condition assessment (both hardware and software perspectives) of sewer and water pipelines and also provides a reference for researchers to further advance the technology in this field. Note to Practitioners-The motivation behind this article was the current challenges and the open issues of the vision system for the condition assessment of sewer and water pipelines. This article presents detailed reviews of the state-of-the-art of the vision system (both hardware and software) and discusses the

关键词： Pipelines inspection Robot sensing systems machine vision Mobile robots automated visual inspection condition assessment machine vision robotic platforms sewer and water pipelines

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A novel vision-based method for loosening detection of marked T-junction pipe fittings integrating GAN-based segmentation and SVM-based classification algorithms

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第6期34卷 2581-2597页

作者： Deng, Xinjian Liu, Jianhua Gong, Hao Huang, Jiayu Beijing Inst Technol Sch Mech Engn 5 South Zhongguancun St Beijing 100081 Peoples R China

Pipes connected by threaded joints are widely applied to transmit fluid and gas in many industries. Loosening in threaded joints causing the problem of fluid or gas leakage may induce disastrous consequences. Regular loosening detection of threaded pipe fittings cannot be overemphasized. In engineering applications, marked bars are drawn on the threaded pipe fittings to indicate loosening/tightening state. Traditional visual inspection requires laborious workloads. An automated method for loosening detection of marked threaded pipe fittings is still lacking. In this paper, a T-junction threaded pipe fitting was chosen as the research object. We proposed a novel vision-based method to conduct the loosening detection of three threaded joints in a T-junction pipe fitting for the first time. Our method contains three integrated modules. A new generative adversarial network-based segmentation module is constructed to accurately segment marked bars first. Then skeleton algorithm is used to extract the center lines of segmented marked bars and three sensitive angle features for loosening detection are constructed. Last, these features are fed into support vector machine-based classification module to differentiate the loosening state from tightening state. The experimental results indicated that the average segmentation accuracy denoted by dice similarity coefficient was 0.96 and the average detection accuracy was 94.7% based on our method. Moreover, our proposed method has been validated having a strong loosening detection ability in different environments, and great potentials in engineering applications.

关键词： Loosening detection Threaded joints Pipe T-junction fitting GAN

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Recent advances in surface defect inspection of industrial products using deep learning techniques

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2021年第1-2期113卷 35-58页

作者： Zheng, Xiaoqing Zheng, Song Kong, Yaguang Chen, Jie Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China

Manual surface inspection methods performed by quality inspectors do not satisfy the continuously increasing quality standards of industrial manufacturing processes. machine vision provides a solution by using an automated visual inspection (AVI) system to perform quality inspection and remove defective products. Numerous studies and works have been conducted on surface inspection algorithms. With the advent of deep learning, a number of new algorithms have been developed for better inspection. In this paper, the state-of-the-art in surface defect inspection using deep learning is presented. In particular, we focus on the inspection of industrial products in semiconductor, steel, and fabric manufacturing processes. This work makes three contributions. First, we present the prior literature reviews on vision-based surface defect inspection and analyze the recent AVI-related hardware and software. Second, we review traditional surface defect inspection algorithms including statistical methods, spectral methods, model-based methods, and learning-based methods. Third, we investigate recent advances in deep learning-based inspection algorithms and present their applications in the steel, fabric, and semiconductor industries. Furthermore, we provide information on publicly available datasets containing surface image samples to facilitate the research on deep learning-based surface inspection.

关键词： machine vision automated visual inspection automated surface inspection Defect inspection Defect detection Deep learning

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automated visual inspection of Fabric Image Using Deep Learning Approach for Defect Detection 4

Automated Visual Inspection of Fabric Image Using Deep Learn...

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Conference on automated visual inspection and machine vision iv

作者： Voronin, V. Sizyakin, R. Zhdanova, M. Semenishchev, E. Bezuglov, D. Zelenskii, A. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Russian Customs Acad Rostov Branch Rostov Na Donu Russia

ISBN: (数字)9781510644090

ISBN: (纸本)9781510644090

As a popular topic in automation, fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. The main challenge for automatically detecting fabric damage, in most cases, is the complex structure of the textile. This article presents a two-stage approach, combining novel and traditional algorithms to enhance image enhancement and defect detection. The first stage is a new combined local and global transform domain-based image enhancement algorithm using block-based alpha-rooting. In the second stage, we construct a neural network based on the modern architecture to detect fabric damage accurately. This solution allows localizing defects with higher accuracy than traditional methods of machine learning and modern methods of deep learning. All experiments were carried out using a public database with examples of damage to the TILDA fabric dataset.

关键词： fabric defect detection image enhancement deep learning segmentation

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Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection 4

Data augmentation and pre-trained networks for extremely low...

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Conference on automated visual inspection and machine vision iv

作者： Gutierrez, Pierre Cordier, Antoine Caldeira, Thais Sautory, Theophile Scortex 22 Rue Berbier du Mets Paris France Univ Estadual Campinas Sch Elect & Comp Engn Campinas Brazil

ISBN: (数字)9781510644090

ISBN: (纸本)9781510644090

The use of deep features coming from pre-trained neural networks for unsupervised anomaly detection purposes has recently gathered momentum in the computer vision field. In particular, industrial inspection applications can take advantage of such features, as demonstrated by the multiple successes of related methods on the MVTec Anomaly Detection (MVTec AD) dataset. These methods make use of neural networks pre-trained on auxiliary classification tasks such as ImageNet. However, to our knowledge, no comparative study of robustness to the low data regimes between these approaches has been conducted yet. For quality inspection applications, the handling of limited sample sizes may be crucial as large quantities of images are not available for small series. In this work, we aim to compare three approaches based on deep pre-trained features when varying the quantity of available data in MVTec AD: KNN, Mahalanobis, and PaDiM. We show that although these methods are mostly robust to small sample sizes, they still can benefit greatly from using data augmentation in the original image space, which allows to deal with very small production runs.

关键词： quality control visual inspection deep learning anomaly detection data augmentation pre-training

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vision threads: a novel approach to generic camera calibration 4

Vision threads: a novel approach to generic camera calibrati...

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Conference on automated visual inspection and machine vision iv

作者： Gauchan, Shishir Bartsch, Jonas Bergmann, Ralf B. Inst Appl Beam Technol BIAS Klagenfurter Str 5 D-28359 Bremen Germany Univ Bremen MAPEX Ctr Mat & Proc Otto Hahn Allee D-28359 Bremen Germany Fac Phys & Elect Engn Otto Hahn Allee D-28359 Bremen Germany

ISBN: (数字)9781510644090

ISBN: (纸本)9781510644090

Optical metrology techniques such as fringe projection technology and phase-measuring deflectometry, which use cameras as image sensors for 3D coordinate measurement and inspection, are becoming increasingly demanding. The necessary precise and at the same time robust calibration of image sensors of these systems is offered by so called vision ray calibration. This generic approach is a model-free description of a vision ray and are determined for each imaging sensor pixel. Numerous images of the sinusoidal fringe images displayed on the active calibration target (LCD) are captured in different poses. This generates a large amount of data and consequently leads to time-consuming evaluation for the camera calibration. In this paper, we propose a new optimization method that uses "vision threads" instead of vision rays to enhance the computation efficiency associated with conventional vision ray calibration. We present MATLAB simulation results in to validate our novel approach.

关键词： Generic Camera Calibration Direct Optimization Computer vision 3D-Metrology Structured Lighting Bundle Adjustment

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