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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51-60 订阅

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

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

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Computers, Information Processing and Advanced Education (CIPAE), 2020 International conference on

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

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

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.

关键词： visualization Image recognition machine vision Metals Glass Optical imaging Classification algorithms Manufacturing Surface treatment Optical devices

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Height estimation clamp by using vision system 18

Height estimation clamp by using vision system

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18th International conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Singhata, Napassadol Faculty of Engineering Rajamangala University of Technology Krungthep Department of Automation and Robotic Engineering Bangkok Thailand

ISBN: (纸本)9780738111278

automated visual inspection is one of these revolutions of industrial technologies. In today's world of technology, a manufacturer requires higher inspection speeds which can be replaced as human inspection. Therefore, the machine vision system plays an important role to develop the machine in the manufacturing process. Thus, it became necessary to develop a low-cost machine for sorting these products in an automated, accurate manner, and to make the system user friendly as possible. This paper presents a machine vision system designed to identify the height estimation to obtain the size of clamps real-time. © 2021 IEEE.

关键词： Computer vision

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An Optimized Lightweight Model for Real-Time Wood Defects Detection based on YOLOv4-Tiny

An Optimized Lightweight Model for Real-Time Wood Defects De...

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2022 International conference on Automatic Control and Intelligent Systems

作者： Lim, Wei-Han Bonab, Mohammad Babrdel Chua, Kein Huat Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Kajang Malaysia

ISBN: (纸本)9781665495813

Many wood manufacturers are still relying on manual human eyes inspection for wood defects detection. This approach is tedious, inconsistent, inefficient, and prone to human errors. machine vision technology can provide a satisfactory solution for wood defects detection and reduce the manpower required. In this paper, a lightweight object detection model is proposed for the detection of four types of wood defects based on the YOLOv4-Tiny architecture. The accuracy of the model is improved by modifying the loss function for YOLOv4-Tiny to incorporate Intersection over Union into its objectness loss. The results showed that the improvement made has successfully enhanced the model's accuracy and the best model can achieve a mean average precision of 88.32% running at 225.22 frames per second.

关键词： convolutional neural networks object detection wood defects yolov4-tiny automated visual inspection

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Classification of municipal solid waste using deep convolutional neural network model applied to multispectral images 4

Classification of municipal solid waste using deep convoluti...

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conference on automated visual inspection and machine vision iv

作者： Muri, Harald Ian D. I. Hjelme, Dag Roar Norwegian Univ Sci & Technol OS Bragstads Plass 2b Trondheim Norway

ISBN: (数字)9781510644090

ISBN: (纸本)9781510644090

Minimization of the environmental impact of the incineration process and to produce energy efficiently are the most important considerations in obtaining efficient operation of waste-to-energy (WtE) plants. WtE operation can obtain significant improvements by predicting combustion properties of municipal solid waste (MSW) prior to incineration. Combustion properties of MSW can be assessed by estimating the weighted waste fractions such as paper and cardboard, plastic or inert and fines. Waste materials and fractions can be recognized using imaging techniques and image classification methods based on deep convolution neural networks (CNN). We have tested a new sensor system for image classification based on using multispectral (MS) images and deep CNN pretrained on the ImageNet database to recognize MSW categories. MS camera was used for sampling images above the walking floor of a WtE plant (StatKraft Varme Tiller, Trondheim). The waste load was automatically registered as industrial or household waste at the time of delivery. The MS images from 49 waste loads were used to perform transfer-learning on EfficientNetB0 model weighted with ImageNet-NoisyStudent parameters. Using the predefined classes, a test and training set were generated from the 49 waste loads delivered between June and September (2020). The training set consisted of 35 waste loads while the remaining 14 waste loads were used as test. The weights for the image feature extraction was constant during training while the fully connected layer (top-layer) was updated for each epoch. The model performance on the test set was assessed by making predictions on the household or industrial waste images. With a fixed threshold value at 0.5, the model showed 85% accuracy, 92% precision, 89% recall and 90% F-measure for industrial class, while for household class the model showed 80% accuracy, 94% precision, 81% recall and 87% F-measure. For all threshold values, the area under curve estimated from the receiver

关键词： material characterization municipal solid waste waste to energy machine vision multispectral imaging deep convolution neural network image classification field testing

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Flux Real-Time Imaging System: High-Speed, High-Throughput automated visual inspection and Defect Detection for Monitoring Flux Dispense

Flux Real-Time Imaging System: High-Speed, High-Throughput A...

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IEEE/SEMI conference and Workshop on Advanced Semiconductor Manufacturing

作者： Chris Edwards Robin Wilcox Darren Vance Meghana Swamy Nancy Xu Package and Test Automation Metrology Systems Analytics & Technology Automation Chandler Arizona Thermal Compression Bond Equipment Assembly & Test Technology Development Chandler Arizona

We present our new Flux Real-Time Imaging System which includes a new machine vision system, calibration utility, user interface, and inspection algorithm. The machine vision system was designed to optimize contrast between flux and no flux regions across various product types and fit within the compact footprint of the existing tool. The calibration utility provides a user interface that walks the user through the tool setup and matching procedure, enabling consistent inspection across tools. The user interface allows the user to create recipes and control the lighting and camera for tool setup as well as live inspection. An advanced machine learning algorithm was developed to identify good, partial flux, and no flux regions for units within the region of interest and alert the user in real-time if the flux deposition quality is not within the acceptable tolerances.

关键词： visualization machine vision Lighting inspection User interfaces Throughput Real-time systems

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A Computer vision Model Utilizing Autoencoders for Surface Defect Recognition 6th

A Computer Vision Model Utilizing Autoencoders for Surface ...

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6th International conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

作者： Pal, Sudipta Bhattacharyya, Rupa Nandi, Sumit Saha, Sourav MOL IT India Pvt Ltd West Bengal Kolkata India Narula Institute of Technology West Bengal Kolkata India Harishchandrapur College West Bengal Kolkata India

ISBN: (纸本)9783031813382

visual steel surface detection plays a crucial role in enhancing the quality of steel sheet manufacturing. However conventional manual inspection-based strategies are subjective and does not comply with increasing quality standards, necessitating the deployment of more efficient automated tools leveraging advanced computer vision techniques. Recent advancements in machine learning models, particularly in automated visual inspection (AVI) methods, have shown promise in detecting and classifying various surface defects using images. However, these classification models encounter challenges due to the limited availability of well-labeled datasets that encompass a wide range of surface defects. In this work, we examine the potential use of autoencoder, a type of self-supervised neural network, along with deployment scheme of decision tree to recognize various types of surface defects that occur during the manufacturing of hot rolled steel sheets. The proposed hierarchical tree structured autoencoder-based multi-classifier model achieves an overall recognition accuracy of 92.33% in distinguishing the various surface defect types in the images of steel sheet by analyzing the reconstruction loss of the images of the same. The experimental results based on the publicly accessible dataset in Kaggle repository demonstrate the effectiveness of the proposed model in comparison with other classification techniques, paving the way for developing autoencoder-based automated tools for similar surface defect recognition in various engineering materials. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Steel sheet

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An automated Technology for IoT based Rail-Track inspection to Locate Surface Flaws by Robotics and Neural Networks 5

An Automated Technology for IoT based Rail-Track Inspection ...

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5th International conference on Inventive Computation Technologies (ICICT)

作者： Maheswari, B. Uma Nithya, P. Vijay, Sreeja Tamilarasi, K. Anitha, G. Muthukumaran, N. PSG Coll Arts & Sci Comp Applicat Coimbatore Tamil Nadu India PSG Coll Arts & Sci Dept Networking & Mobile Applicat Coimbatore Tamil Nadu India SA Engn Coll Dept ECE Chennai Tamil Nadu India Jeppiaar Inst Technol Dept CSE Chennai Tamil Nadu India SIMATS Saveetha Sch Engn Dept ECE Chennai Tamil Nadu India Francis Xavier Engn Coll Dept ECE Tirunelveli India Coll Sci & Arts Dept Comp Sci University U Arab Emirates

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

To maintain national security, railway transportation needs regular examinations and quick servicing. Conventional manual screenings are not always time-consuming and costly, but they also depend on individual skill and effectiveness at the time of survey to discover defects accurately. It is possible to automate Internet of Things (IoT)-based solutions using technology and automation, for example, machinery installed on train lines or other difficult-to-reach areas and handled through control rooms. A revolutionary automatic model based on robots and visually inspecting is suggested in this research. While inspecting, the technology enables for on-site image analysis, cloud services for photographs of broken railway tracks, and robot localization within a 3-6-inch range. The suggested method relates a cutting-edge machine Learning technique to pictures received from the rails in order to identify them as regular or dangerous. Such places are then identified, and a specialized operator with a small number of sites to examine can do a more thorough examination.

关键词： Railtrack inspection visual inspection computer vision convolutional neural network

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

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

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Big Data, Cloud Computing, Data Science & Engineering (BCD)

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

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.

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Shell Autonomous Integrity Recognition - machine vision Application for inspections

Shell Autonomous Integrity Recognition - Machine Vision Appl...

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Abu Dhabi International Petroleum Exhibition and conference 2022, ADIPEC 2022

作者： Blom, David Berchenko, Ilya Samie, Farid Frajberg, Darian Shell plc C3.Ai

ISBN: (纸本)9781613998724

Shell Autonomous Integrity Recognition (AIR) on C3 AI Platform is an Artificial Intelligence (AI) application that allows pressure equipment and structural steel inspectors to quickly and easily make use of automated image capture and evaluation to support execution of external integrity inspections. By processing data in the cloud coming from inspections carried out with handheld devices, drones and robots, this application enables inspectors to objectively evaluate issues, identify items that may have been overlooked, reduce the time needed to generate reports, and improve inputs to maintenance planning. By using Shell Autonomous Integrity Recognition on C3 AI Platform, users can improve the quality, efficiency, and standardization of visual inspections. Copyright © 2022, Society of Petroleum Engineers.

关键词： inspection

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Utilization of Computer vision Technique for automated Crack Detection Based on UAV-Taken Images 1st

Utilization of Computer Vision Technique for Automated Crack...

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1st conference of the European Association on Quality Control of Bridges and Structures, EUROSTRUCT 2021

作者： Mirzazade, Ali Nodeh, Maryam Pahlavan Popescu, Cosmin Blanksvärd, Thomas Täljsten, Björn Luleå University of Technology Luleå Sweden SINTEF Narvik AS Narvik8517 Norway

ISBN: (纸本)9783030918767

Conventional bridge inspection is usually performed by experienced engineers, trying to detect and document damage patterns manually. By increased number of built Bridges, there is a growing interest in automated damage detection methods. Therefore, the field of autonomous bridge inspection with the application of machine learning techniques on UAV-taken images is gaining popularity. Due to recent technological advancement, a large number of datasets can be collected, with a high rate of productivity and accuracy, to train convolutional neural networks (CNNs) leading us to automated Structural health monitoring (SHM). In this paper, a case study is chosen to scan two times with almost one year as a time interval. In the first scanning, dataset was gathered to train four different CNNs. Then, the performance of CNNs was compared for the purpose of autonomous crack detection in the second round of scanning. Models evaluated on a number of performance metrics, namely- (i) accuracy, (ii) loss, (iii) computation time, (iv) model size, and (v) architectural depth. Finally, the performance of studied CNNs is discussed, which can lead researchers in the Transfer-Learning approach to generate a model for damage detection with a limited number of datasets prepared in the first turn of bridge inspection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crack detection

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