Active Learning for Automated Visual Inspection of Manufactured Products
University of Ljubljana（卢布尔雅那大学）；Jožef Stefan International Postgraduate School（约热夫·斯特凡国际研究生院）；Philips Consumer Lifestyle BV（飞利浦消费生活方式有限公司）；Qlector d.o.o.（Qlector有限责任公司）；Jožef Stefan Institute（约热夫·斯特凡研究所）
Elena Trajkova, Jože M. Rožanec, Paulien Dam, Blaž Fortuna, Dunja Mladenić
Quality control is a key activity performed by manufacturing enterprises to ensure products meet quality standards and avoid potential damage to the brand's reputation. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing. In addition, artificial intelligence enables higher degrees of automation, reducing overall costs and time required for defect inspection. In this research, we compare three active learning approaches and five machine learning algorithms applied to visual defect inspection with real-world data provided by Philips Consumer Lifestyle BV. Our results show that active learning reduces the data labeling effort without detriment to the models' performance.
Prime University（Prime大学）；Barisal University（巴里萨尔大学）；Independent University, Bangladesh（孟加拉国独立大学）
Quality control at each stage of production in textile industry has become a key factor to retaining the existence in the highly competitive global market. Problems of manual fabric defect inspection are lack of accuracy and high time consumption, where early and accurate fabric defect detection is a significant phase of quality control. Computer vision based, i.e. automated fabric defect inspection systems are thought by many researchers of different countries to be very useful to resolve these problems. There are two major challenges to be resolved to attain a successful automated fabric def…
A critical aspect in the manufacturing process is the visual quality inspection of manufactured components for defects and flaws. Human-only visual inspection can be very time-consuming and laborious, and is a significant bottleneck especially for high-throughput manufacturing scenarios. Given significant advances in the field of deep learning, automated visual quality inspection can lead to highly efficient and reliable detection of defects and flaws during the manufacturing process. However, deep learning-driven visual inspection methods often necessitate significant computational resources,…
More than a thousand 8" silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Calorimeter for the CMS experiment at CERN. A deep learning-based algorithm that pre-selects potentially anomalous images of the sensor surface in real time has been developed to automate the visual inspection. The anomaly detection is done by an ensemble of independent deep convolutional neural networks: an autoencoder and a classifier. The performance is evaluated on images acquired in production. The pre-selection r…
Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）；Jožef Stefan Institute（约瑟夫·斯特凡研究所）；Qlector d.o.o.（Qlector有限责任公司）；Faculty of Electrical Engineering, University of Ljubljana（卢布尔雅那大学电气工程学院）；Philips Consumer Lifestyle BV（飞利浦消费生活方式公司）
Quality control is a crucial activity performed by manufacturing enterprises to ensure that their products meet quality standards and avoid potential damage to the brand's reputation. The decreased cost of sensors and connectivity enabled increasing digitalization of manufacturing. In addition, artificial intelligence enables higher degrees of automation, reducing overall costs and time required for defect inspection. This research compares three active learning approaches, having single and multiple oracles, to visual inspection. Six new metrics are proposed to assess the quality of calibrati…
A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection
School of Computing, Telkom University（Telkom大学计算机学院）；Faculty of Engineering and Technology, School of Computing and Artificial Intelligence（工程与技术学院，计算与人工智能学院）
Automated Cervical Cancer Detection through Visual Inspection with Acetic Acid in Resource-Poor Settings with Lightweight Deep Learning Models Deployed on an Android Device
School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）；Manipal School of Information Sciences, Manipal Academy of Higher Education（曼尼帕尔高等学院信息科学学院）；Department of Obstetrics and Gynecology, Kasturba Medical College（卡斯特尔巴医学院妇产科部门）；NMAM Institute of Technology, Nitte（尼特学院纳米姆技术学院）；Manipal Institute of Technology, Manipal Academy of Higher Education（曼尼帕尔高等学院技术学院）
Niramai Health Analytix Pvt Ltd（Niramai健康分析私人有限公司）
In the last two years, millions of lives have been lost due to COVID-19. Despite the vaccination programmes for a year, hospitalization rates and deaths are still high due to the new variants of COVID-19. Stringent guidelines and COVID-19 screening measures such as temperature check and mask check at all public places are helping reduce the spread of COVID-19. Visual inspections to ensure these screening measures can be taxing and erroneous. Automated inspection ensures an effective and accurate screening. Traditional approaches involve identification of faces and masks from visual camera imag…
Korea Institute of Science and Technology Europe Forschungsgesellschaft mbH（韩国科学技术研究院欧洲有限公司）；TU Kaiserslautern（凯泽斯劳滕工业大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
The demands of automated shipping address recognition and verification have increased to handle a large number of packages and to save costs associated with misdelivery. A previous study proposed a deep learning system where the shipping address is recognized and verified based on a camera image capturing the shipping address and barcode area. Because the system performance depends on the input image quality, inspection of input image quality is necessary for image preprocessing. In this paper, we propose an input image quality verification method combining global and local features. Object de…
Profactor GmbH（Profactor有限公司）；JKU Institute of Computational Perception（约翰内斯·开普勒大学计算感知研究所）
Automated fiber placement (AFP) is an advanced manufacturing technology that increases the rate of production of composite materials. At the same time, the need for adaptable and fast inline control methods of such parts raises. Existing inspection systems make use of handcrafted filter chains and feature detectors, tuned for a specific measurement methods by domain experts. These methods hardly scale to new defects or different measurement devices. In this paper, we propose to formulate AFP defect detection as an image segmentation problem that can be solved in an end-to-end fashion using art…
Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networks
imec-Vision Lab（imec-Vision实验室）；University of Antwerp（安特卫普大学）；DynXlab: Center for 4D Quantitative X-ray Imaging and Analysis（DynXlab：四维定量X射线成像与分析中心）；Otto von Guericke University（奥托·冯·格里克大学）；Genomics Research Centre, Human Technopole（人类技术研究院基因组学研究中心）；Materialise NV.（Materialise NV.公司）；Flanders Make vzw.（佛兰德制造vzw.机构）
Machine Learning-Based Ultrasonic Weld Characterization Using Hierarchical Wave Modeling and Diffusion-Driven Distribution Alignment
Data Science Group, Los Alamos National Laboratory, Los Alamos NM, USA（数据科学组，洛斯阿拉莫斯国家实验室）；Engineering Institute, Los Alamos National Laboratory, Los Alamos NM, USA（工程学院，洛斯阿拉莫斯国家实验室）
