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

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Shape-Adaptive Lighting for Complex Geometries in automated visual inspection 2nd

Shape-Adaptive Lighting for Complex Geometries in Automated ...

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2nd Modeling, Estimation and Control conference (MECC)

作者： Gerges, Mark Chen, Xu Univ Washington Dept Mech Engn Seattle WA 98105 USA

Automating visual inspection has the potential to remove the current bottleneck of burdensome and subjective manual inspection in precision manufacturing. However, complex geometries of parts hinder uniform illumination, and high reflectivity challenges accurate focusing for digital visual data collection. This paper provides a novel method to distribute consistent illuminance for quality image data collection over complex-shaped, highly reflective surfaces. The central approach entails using arrays of independently controlled light sources to generate different lighting patterns, structures, and colors. Such results consider the geometry and 3D pose of objects in the environment and the surface topography of the work-piece to be inspected, leading to greater imaging qualities for precision inspection. This paper discusses the mathematical problem formulation, the analytic solution, the optimality of the proposed shape-adaptive lighting, and experimental results in imaging curved parts common in precision manufacturing. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Automatic inspection machine vision Lighting Illuminance Distribution

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A Supervised Quantification of the Color Names Characterizing the visual Component Color in the ABCD Dermatological Criteria for a Further Melanoma inspection 8

A Supervised Quantification of the Color Names Characterizin...

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8th International conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE)

作者： Daghrir, Jinen Tlig, Lotfi Bouchouicha, Moez Litaiem, Noureddine Zeglaoui, Faten Sayadi, Mounir Univ Sousse ISITCom Hammam Sousse 4011 Tunisia Univ Tunis ENSIT Lab SIME Tunis Tunisia Univ Toulon & Var Aix Marseille Univ CNRS LIS Toulon France Univ Tunis El Manar Charles Nicolle Hosp Fac Med Tunis Dept Dermatol Tunis Tunisia

ISBN: (纸本)9789897585661

Digital imaging is widely used for creating automated systems for medical purposes such as the diagnosis of certain kinds of diseases. One typical use of these computer vision diagnosis systems in dermatology is the inspection of melanoma skin cancer, which is one of the most fatal skin cancer. For the early detection of melanoma, a lot of systems have been proposed. Most of them use some visual features through image processing methods, such as color processing and border and texture inspection. Color variation is a good clue to differentiate melanoma and benign lesions. Thus, it is important to process skin lesion images to extract the various colors. The paper presents a new method that extracts the different color names from a skin lesion in a supervised way based on observed skin condition types. These features can ensure accurate melanoma detection with other types of features. To demonstrate the effectiveness of our suggested representation, we construct a prediction system for inspecting the malignancy of skin lesions. The experimental results show a consistent improvement in the prediction performance against other color representations.

关键词： Melanoma inspection Medical Imaging Color Name Extraction machine Learning Computer vision Image Processing

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machine vision Based Autonomous Cruise inspection Vehicle Hole Recognition and Positioning System

Machine Vision Based Autonomous Cruise Inspection Vehicle Ho...

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Energy Engineering and Power Systems (EEPS), International conference on

作者： Shuofeng Niu Yanda Wu Yafeng Zhao Zhen Li Qiwei Zhao Wanshu Xu Yingnan Liu Substation Operation and Maintenance Center Heze Power Supply Company of State Grid Shandong Electric Power Company Heze China School of Electrical Engineering Shandong University Jinan China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

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： Motor drives visualization machine vision Switches inspection visual systems Maintenance Safety Automobiles Switching circuits

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Motion Blur Impact on Measurement in Autonomous inspection

Motion Blur Impact on Measurement in Autonomous Inspection

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IEEE/ASME International conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Nahuel Jose Gabriele Marchello Ferdinando Cannella Italian Institute of Technology Genoa Italy

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

This paper investigates the application of mobile robotic platforms for visual data capture in infrastructure inspection tasks. The captured data offer significant value for both manual and automated inspection processes. It can produce detailed visual information for human inspectors and serve as input for automated systems to detect anomalies or assist inspectors through computer-aided analysis. Additionally, these data can be integrated into the robot navigation system for real-time path optimisation. A critical challenge in optimising data capture is highlighted: balancing the desired precision with the time invested in inspections. The study explores this trade-off by analysing the impact of motion blur on measurement errors. Capturing high-quality images with minimal motion blur necessitates slower inspection speeds. The findings suggest that for extensive inspection areas, prioritising mid-range object distances can optimise data capture, as errors increase at a slower pace at these distances compared to closer or farther ranges. This research paves the way for further advancements. Future areas of exploration include evaluating noise reduction techniques, incorporating real-world complexities into testing environments, and investigating the impact of capture speed on machine learning algorithms.

关键词： Image quality visualization machine learning algorithms Accuracy machine vision System performance inspection Reliability engineering Motion measurement Testing

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The effects of pattern screen surface deformation on deflectometric measurements - a simulation study 4

The effects of pattern screen surface deformation on deflect...

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conference on automated visual inspection and machine vision iv

作者： Allgeier, Stephan Gengenbach, Ulrich Koehler, Bernd Reichert, Klaus-Martin Hagenmeyer, Veit Karlsruhe Inst Technol KIT Inst Automat & Appl Informat Postfach 3640 D-76021 Karlsruhe Germany

ISBN: (数字)9781510644090

ISBN: (纸本)9781510644090

Phase-measuring deflectometry (PMD) is an optical inspection technique for full-field topography measurements of reflective sample surfaces. The measurement principle relies on the analysis of specific patterns, reflected at the sample surface. Evaluation algorithms often model the respective pattern screen as a planar light source. However, the 32 '' pattern screen in our inspection setup exhibits a central bulge of its surface of about 2-3 mm. This paper presents a simulation framework for PMD to evaluate the effects of a deformed screen surface. The idea is to simulate image data acquired with screen surface deformations and to examine the effects on the PMD evaluation results. The simulated setup consists of a 32 '' pattern screen with an adjustable central bulge height of 0-3 mm and two cameras with a field of view (FOV) of approximately 225 mm by 172 mm on the sample surface. A first experiment examines the reconstruction errors for a planar sample surface if the reconstruction algorithm uses perfect calibration data (i.e. the same parameters used for the simulated image acquisition). The reconstructed surfaces exhibit a tilt with a maximum height difference of 174 mu m across the FOV. A second experiment repeats the reconstruction process of the same sample surface, using camera parameters determined in a simulated calibration process. The resulting surfaces possess irregular, wave-like errors with amplitudes of up to 9 mu m in the FOV. The presented simulation results reveal the accuracy limits if a deformation model of the pattern screen is not explicitly included in the reconstruction process.

关键词： surface inspection deflectometry simulation printed electronics calibration

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Intelligent machine vision Implementation for Production Quality Control

Intelligent Machine Vision Implementation for Production Qua...

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Open Innovations Association (FRUCT), conference of

作者： Anton ivaschenko Vladimir Avsievich Yuriy Reznikov Andrey Belikov Vera Turkova Pavel Sitnikov Oleg Surnin Software Engineering Company “Open code” Samara Russia

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.

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Conceptual Design of an automated Workstation for the Control of Manufactured Products in Single-Purpose machines 1

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5th International conference on Design, Simulation, Manufacturing: The Innovation Exchange, DSMIE 2022

作者： Bohušík, Martin Bulej, Vladimír Kuric, ivan Sága, Milan Stenchlák, Vladimír University of Žilina 8215/1 Univerzitná Street Žilina Slovakia

ISBN: (数字)9783031060250

ISBN: (纸本)9783031060243

The article describes the solution of an automated line for the industrial company, where there was a requirement for inspection of manufactured parts and inspection of assembly of parts, including the design of an automated workstation (robotic workcells, assembly stations, inspection stations, operators, etc.). Camera systems that use artificial intelligence to determine the contour can check the shape of the gears involved. The main requirement to control production by camera systems arose because of human resources’ time-consuming nature of the visual inspection. The article provides an overview of camera systems used in engineering companies that capture images of objects, and then these images are further processed in detail through software tools. Properly designing the automated workplace inspection through cameras consisted of collecting data from the company - lighting in production, material reflectivity, etc. Based on the obtained data, specific types of cameras from specific manufacturers were selected and placed at the necessary locations in the automated production. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： automated manufacturing Camera systems machine vision R&D Investment Sustainable manufacturing

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Research on visual inspection Technology in Automatic Assembly for Manhole Cover of Rocket Fuel Tank 4

Research on Visual Inspection Technology in Automatic Assemb...

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4th International conference on Advances in Computer Technology, Information Science and Communications, CTISC 2022

作者： Zhang, Xiaolin Wang, Zanqin Yu, Hang Liu, Mei Xing, Baiqiang Harbin Institute of Technology School of Instrumentation Science and Engineering Harbin China Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing Robotics School of Mechatronic Engineering and Automation Shanghai China Capital Aerospace Machinery Corporation Limited Beijing China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

Manholes are openings on aerospace vehicles for people to enter the cabin for maintenance tasks. The covers of these openings are called manhole *** assembly quality of the manhole cover is related to the success or failure of refueling and flight in the rocket range. At present, the cover assembly is done manually, which is inefficient and error-prone. There is an urgent need for an automated assembly system, replacing manual assembly to complete the tasks. In this paper, an automatic assembly system for the manhole cover of rocket fuel tank is proposed and the visual inspection technology is mainly explained. The study uses machine vision and image processing with robots to meet actual assembly requirements, which is of great significance to assembly technology in the aerospace field. © 2022 IEEE.

关键词： Assembly

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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...

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conference on Power, Energy and Innovations (ICPEI)

作者： Wichian Ooppakaew Boonyarit Kumkiet Jakkree Srinonchat dept. of electronics and telecommunication engineering Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani Thailand

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.

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Effective Deep Learning Training Method Using Blur and Noise Filter To Detect Defect in TFT-LCD PAD 4

Effective Deep Learning Training Method Using Blur and Noise...

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conference on automated visual inspection and machine vision iv

作者： Lee, JaeGu Yoon, Yeo Min Kim, Seon Geol Ha, Chang Woo Yoon, Seong Baek Lee, Hyo Jin Deep Learning Res Lab 819 Eonju Ro Seoul South Korea

ISBN: (数字)9781510644090

ISBN: (纸本)9781510644090

In this paper, we propose a preprocessing method of exploiting noise and blur for effective noise elimination in data. At present, there are many kinds of research to improve the performance of object classification, detection, and image segmentation based on deep learning. For instance, adding noise to data, multiple in-depth convolution layers, and data augmentation have been studied in many ways. An in-depth convolution network results in long processing time and data augmentation gives a burden to memory usage. However, adding noise and blur data preprocessing method gives less burden to hardware, which helps improve algorithm performance. The proposed method is applied to TFT-LCD (Thinfilm Transistor Liquid Crystal Display) PAD defect detection for improved performance. To verify the accuracy and repeatability, 691 actual defect images are used in experiments. These images are composed of complex patterns and defects in the images having barely 2 pixels with little intensity difference. To confirm which filters are better, Gaussian blur, Salt & Pepper noise, and Gaussian noise filters are used for comparison. According to the result, the experiments with Salt & Pepper and Gaussian noise detect all defects. However, the repeatability of the Gaussian noise filter seems better than that with Salt & pepper. Furthermore, applying noise and blur to train data shows more than twice higher detection accuracy than those without such applications. We verified that using Gaussian noise and blur indicates excellent accuracy and repeatability when inspecting the TFT-LCD PAD area in AOI machines.

关键词： Deep Learning Effective Training Method PAD inspection Defect detection

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