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检索条件"任意字段=Conference on Automated Visual Inspection and Machine Vision IV"

共 294 条记录，以下是1-10 订阅

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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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automated visual inspection and machine vision iv

Automated Visual Inspection and Machine Vision IV

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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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Attention Modules Improve Image-Level Anomaly Detection for Industrial inspection: A DifferNet Case Study

Attention Modules Improve Image-Level Anomaly Detection for ...

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IEEE/CVF Winter conference on Applications of Computer vision (WACV)

作者： Vieira E Silva, Andre Luiz Simties, Francisco Kowerko, Danny Schlosser, Tobias Battisti, Felipe Teichrieb, Veronica Univ Fed Pernambuco Ctr Informat Voxar Labs Recife PE Brazil Tech Univ Chemnitz Jr Professorship Media Comp Chemnitz Germany Univ Fed Rural Pernambuco Visual Comp Lab DC Recife PE Brazil

ISBN: (纸本)9798350318920;9798350318937

Within (semi-)automated visual industrial inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery. The emergence of these often rarely occurring defect patterns explains the general need for labeled data corpora. To alleviate this issue and advance the current state of the art in unsupervised visual inspection, this work proposes a DifferNet-based solution enhanced with attention modules: AttentDifferNet. It improves image-level detection and classification capabilities on three visual anomaly detection datasets for industrial inspection: InsPLAD-fault, MVTec AD, and Semiconductor Wafer. In comparison to the state of the art, AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quali-quantitative study. Our quantitative evaluation shows an average improvement compared to DifferNet - of 1.77 +/- 0.25 percentage points in overall AUROC considering all three datasets, reaching SOTA results in InsPLAD-fault, an industrial inspection in-the-wild dataset. As our variants to AttentDifferNet show great prospects in the context of currently investigated approaches, a baseline is formulated, emphasizing the importance of attention for industrial anomaly detection both in the wild and in controlled environments.

关键词： Algorithms Algorithms and algorithms Applications formulations Image recognition and understanding machine learning architectures Remote Sensing

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Intelligent machine vision Implementation for Production Quality Control 34

Intelligent Machine Vision Implementation for Production Qua...

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34th conference of Open Innovations Association FRUCT, FRUCT 2023

作者： ivaschenko, Anton Avsievich, Vladimir Reznikov, Yuriy Belikov, Andrey Turkova, Vera Sitnikov, Pavel Surnin, Oleg Software Engineering Company 'Open Code' Samara Russia

ISBN: (纸本)9789526524603

The paper presents a quality guarantor solution for modern production enterprises based on the combined use of artificial neural networks within a distributed machine vision platform. The platform includes a set of hardware and software modules for visual technical quality control of production processes. There are presented two typical modules for visual inspection of product external appearance and internal parts, spaces and openings using an endoscope video camera. Digital platform software is built for semantic and statistical processing of Big Data characterizing the state of the technological process. The results of the solution implementation in practice are illustrated by several cases of automated visual control of products, automatic verification of the presence of structural fasteners, automated sorting of products, and inspection of internal surfaces and recognizing of holes. Implementation of quality guarantor architecture and design technique based on covering the production process with the modules of external and internal visual quality control allows improving the quality indicators due to systematic and targeted application of intelligent machine vision. © 2023 FRUCT Oy.

关键词： Computer vision

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Free-Viewpoint visual inspection via 3D Gaussian Splatting for Direct Template Matching 50

Free-Viewpoint Visual Inspection via 3D Gaussian Splatting f...

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50th Annual conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Ito, Kenta Ueda, Shiori Mori, Shohei Sugano, Junichi Adachi, Hideyuki Saito, Hideo Information and Computer Science Keio University Yokohama Japan Institute of Computer Graphics and Vision Graz University of Technology Graz Austria ViSCO Technologies Corporation Tokyo Japan

ISBN: (纸本)9781665464543

machine vision systems play a pivotal role in streamlining manufacturing processes, notably in quality control through automatic in-line visual inspections. A common practice for inspecting parts, components, and final products is to use a master part benchmark for quality comparison. However, challenges arise when objects enter inspection points in unintended orientations. This misalignment potentially leads to erroneous decisions by automated systems, resulting in additional checkpoints or wastage affecting the production rate. To tackle this issue, we propose a visual inspection pipeline that leverages recent machine learning-based approaches to compare the inspection target and a master part virtually oriented to the same perspective. Specifically, we suggest combining 3D Gaussian Splatting and DUSt3R as a practical solution. Our approach demonstrates its efficacy in real-world scenarios through testing on three mock parts and a real industrial component. © 2024 IEEE.

关键词： Template matching

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Using DLT for Textile Fabric inspection: A Novel Network for Detecting Fabric Defects 2

Using DLT for Textile Fabric Inspection: A Novel Network for...

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2nd IEEE International conference on vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Gandhimathinathan, A. Abishek, B. Balaji, R. Dhilip, T. Sabarisrinivas, K.R. Saveetha Engineering College Department of Electronics and CommunicationEngineering Chennai India

ISBN: (纸本)9798350347982

This research paper proposes a novel network that uses Distributed Ledger Technology (DLT) for automated textile fabric inspection to detect fabric defects accurately and efficiently. The traditional method of visual inspection by human operators is subjective, time-consuming, and prone to errors, resulting in low productivity and increased cost. automated inspection methods, such as machine vision and machine learning, can help improve the quality of textile products by providing a more objective and accurate inspection process. DLT can provide an additional layer of security and transparency to the fabric inspection process. Overall, the proposed system has the potential to improve the efficiency, accuracy, and reliability of the textile fabric inspection process. © 2023 IEEE.

关键词： Transparency

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Recognition and Analysis of Glass Subsurface Defects by Integrating machine vision and Image Processing Technology 5

Recognition and Analysis of Glass Subsurface Defects by Inte...

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5th International conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Lin, Chen Ling, Xiong Luo, Guangming Wuhan University of Science and Technology Wuhan China Changli Glass Honghu Co. Ltd Wuhan China

ISBN: (纸本)9798331527662

machine vision and computer image processing technologies are widely used in the metallurgical industry, especially in recognizing and analyzing defects in glass. High surfaces of planer surface and quality in the glass and metal manufacturing sector primarily require automated and performance visual detection, and inspection systems and algorithms are constantly improving. This article, therefore, seeks to comprehensively recognize and analyze glass subsurface defects through the integration of image processing technologies and machine vision. According to the algorithm's properties and the image features typified in the process, the defects of identified glass components are primarily classified based on determining the cause and the optical features of characteristics exhibited. Given the nature of varied and detected effects determined mainly by the classifiers, an improved accuracy level was notable, and a reduction of the computational complexity was evident following the use of machine vision and image processing technologies. © 2024 IEEE.

关键词： machine vision

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machine vision based autonomous cruise inspection vehicle hole recognition and positioning system 3

Machine vision based autonomous cruise inspection vehicle ho...

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3rd International conference on Energy and Electrical Power Systems (ICEEPS)

作者： Niu, Shuofeng Wu, Yanda Zhao, Yafeng Li, Zhen Zhao, Qiwei Xu, Wanshu Liu, Yingnan State Grid Shandong Elect Power Co Heze Power Supply Co Substn Operat & Maintenance Ctr Heze Peoples R China Shandong Univ Sch Elect Engn Jinan Peoples R China

ISBN: (纸本)9798350375145;9798350375138

A machine vision based autonomous cruise inspection car hole recognition and positioning system is designed to address the issues of low operating efficiency, high labor intensity, and potential safety accidents in high-voltage switchgear maintenance cars. Using a visual system to take photos of the fixed holes on the switchgear, image filtering, hole processing, circular fitting, and other processing are performed on the collected images to obtain the position information of the holes. Combined with the algorithm for calculating the angle between the positioning holes on the switchgear and the algorithm for coordinate positioning of the holes, the rotation angle and displacement of the motor control system are obtained. The experiment was conducted using a circular hole with a diameter of 8mm, and the results showed that the accuracy of hole alignment and positioning reached 100%, which can meet the automated detection requirements of hole recognition and alignment for maintenance vehicles.

关键词： machine vision maintenance car hole position recognition autonomous cruise control

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machine vision inspection of Object Positioning in a Hard Disk Assembly Plant

Machine Vision Inspection of Object Positioning in a Hard Di...

引用

2023 International conference on Power, Energy and Innovations, ICPEI 2023

作者： Ooppakaew, Wichian Kumkiet, Boonyarit Srinonchat, Jakkree Dept. of Electronics and Telecommunication Engineering Pathum Thani Thailand

ISBN: (纸本)9798350344585

machine vision is a field of computer vision that focuses on developing and implementing automated visual inspection systems. It involves using cameras, sensors, and image processing algorithms to capture and analyze images or video data for various applications. machine vision enables object detection, recognition, measurement, and quality control, enhancing industry efficiency and accuracy, mainly focusing on object positioning inspection in this research. Object positioning inspection using machine vision has become critical in various industrial applications requiring precise object alignment and positioning. This review explores the fundamental principles, challenges, and applications of object positioning inspection, including detecting misalignment, rotation, translation, and scaling. The proposed primary objective of object positioning inspection is to ensure that objects are correctly positioned with reference points or predefined geometric parameters applied in the hard disk component assembly factory. The edge detection and decision function are operated with 1,000 images captured from the real-time camera. The experiment result indicated that the average accuracy is 95.27% for object positioning inspection. Additionally, this proposal identifies current research trends and future directions for advancing object positioning inspection using machine vision for industrial applications. © 2023 IEEE.

关键词： inspection

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A machine Learning-based Approach for Automatic Grading and Quality inspection of Indian Mangoes 2

A Machine Learning-based Approach for Automatic Grading and ...

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2nd IEEE-Industrial-Electronics-Society Annual On-Line conference (ONCON)

作者： Bagchi, Sourav Aditya, Janumpally Varun Kumari, Sneha Dhanraj, Malla Jenamani, Mamata Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur W Bengal India

ISBN: (纸本)9798350357974

Manual visual assessment of mangoes has been problematic for the agriculture sector because of its time-consuming nature and inconsistent evaluation and sorting methods. The advent of automated flaw identification using computer vision and machine learning offers a notable shift and improvement in the visual inspection process. A common issue with mangoes is the presence of dark patches, indicative of disease or rot, which negatively affect the appearance and quality of the fruit. This paper introduces a framework using computer vision which utilizes image analysis and machine learning methods to identify these dark spots, taking into account the mangoes' texture. The proposed framework has a simplified configuration and tuning process, enhancing its ease of deployment in real-world applications. This innovation aligns with the advancements in integrating cutting-edge technologies to optimize efficiency and consistency in agricultural practices, thereby contributing to the evolution of smart agriculture and addressing the challenges and opportunities presented by the next wave of industrial revolution.

关键词： Computer vision System Dark Patches detection Image Processing Local Binary Pattern (LBP) machine Learning Random Forest SVM Classifier Grading

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