scholarly journals A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 683
Author(s):  
José L. Escalona ◽  
Pedro Urda ◽  
Sergio Muñoz
Keyword(s):  
Computer Vision ◽  
Inertial Sensors ◽  
Vision System ◽  
Accurate Method ◽  
Sensor Data ◽  
Measuring System ◽  
Railway Vehicles ◽  
Track Geometry ◽  
Video Cameras ◽  
Measuring Unit

This paper describes the kinematics used for the calculation of track geometric irregularities of a new Track Geometry Measuring System (TGMS) to be installed in railway vehicles. The TGMS includes a computer for data acquisition and process, a set of sensors including an inertial measuring unit (IMU, 3D gyroscope and 3D accelerometer), two video cameras and an encoder. The kinematic description, that is borrowed from the multibody dynamics analysis of railway vehicles used in computer simulation codes, is used to calculate the relative motion between the vehicle and the track, and also for the computer vision system and its calibration. The multibody framework is thus used to find the formulas that are needed to calculate the track irregularities (gauge, cross-level, alignment and vertical profile) as a function of sensor data. The TGMS has been experimentally tested in a 1:10 scaled vehicle and track specifically designed for this investigation. The geometric irregularities of a 90 m-scale track have been measured with an alternative and accurate method and the results are compared with the results of the TGMS. Results show a good agreement between both methods of calculation of the geometric irregularities.

Computer-Vision-gestütztes Einmessen von Rohteilen/Computer-Vision-aided measuring of parts – Development of a robust camera-based measuring system in a 3-axis-machiningcenter

2021 ◽  
Vol 111 (09) ◽  
pp. 654-658
Author(s):  
Jonas Bien ◽  
Julius Weihe ◽  
Jakob Wagner ◽  
Thomas Lagemann ◽  
Elke Hergenröther ◽  
...  
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Measuring System ◽  
Computer Vision System ◽  
Research Project ◽  
Critical Part

Bei CNC-gesteuerten Bearbeitungszentren ist das Erfassen der Rohteilposition und -abmessungen im Arbeitsraum der Werkzeugmaschine ein wichtiger Arbeitsschritt beim Rüstprozess vor dem Fräsvorgang. Meist wird zum Einmessen des Rohteils ein Messtaster verwendet, der das Rohteil in seinen X- und Y- Ausmaßen, in der Z-Höhe und der Rotation vermisst und diese Maße an die Steuerung der Werkzeugmaschine übergibt. Im durch die Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz („Loewe“) des Landes Hessen geförderten Forschungsvorhaben „Smarte Aufspannkontrolle“ wurde ein Computer-Vision-System entwickelt, dass das Erfassen von Rohteilposition und -abmessungen mit einer Kamera erlaubt und damit den Rüstprozess vereinfacht, beschleunigt und weniger anfällig gegenüber Bedienungsfehler macht.   In CNC-Mills, recording the position and dimension of raw parts is a critical part of the process. In modern milling-centres, 3D-Touchprobes are used to detect part dimensions and locations. In the research project „Smarte Aufspannkontrolle“ funded by “Loewe” (Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz), a computer vision system (CVS) was developed. The CVS is able to detect part dimensions and locations. Its purpose is to make the part-setup quicker and to lower the risk of human mistakes.  

scholarly journals Marbling Grading Framework Applied on Meat Boutique Environment

2016 ◽  
Author(s):  
Saulo Martiello Mastelini ◽  
Matheus Camilo Da Silva ◽  
Ana Paula Ayub da Costa Barbon ◽  
Sylvio Barbon Jr.
Keyword(s):  
Computer Vision ◽  
Quality Evaluation ◽  
Vision System ◽  
Accurate Method ◽  
Analysis Tool ◽  
Production Environment ◽  
Computer Vision System ◽  
Beef Quality ◽  
Visual Approach ◽  
Food Market

Bovine meat commercialization has an important role in the general food market scenario. The beef quality evaluation is realized through many ways, being one of the parameters the intramuscular fat amount (marbling). This evaluation is often made by a visual approach, so the process is subjective and susceptible to some errors sources. The use of Computer Vision techniques results in an automatized, non-subjective, fast and accurate method for evaluation. This paper presents the modeling and development of a Computer Vision System for Marbling evaluation, applied on a meat Boutique, localized in Londrina – PR. The proposed System uses a Computer Vision approach to control the features of the marbling analysis tool, aiming to satisfy sanitary requirements for non-contamination of the analyzed samples. Besides that, multiples samples on the scene are supported by our application. The proposed Computer Vision System has proved to be suitable for implantation in a production environment, like a meat Boutique.

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

scholarly journals Measurement of Gem Colour Using a Computer Vision System: A Case Study with Jadeite-Jade

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 791
Author(s):  
Sufei Zhang ◽  
Ying Guo
Keyword(s):  
Computer Vision ◽  
Regression Model ◽  
Vision System ◽  
Expert Group ◽  
Group Test ◽  
System A ◽  
Real Objects ◽  
Practical Tool ◽  
Almost All

This paper introduces computer vision systems (CVSs), which provides a new method to measure gem colour, and compares CVS and colourimeter (CM) measurements of jadeite-jade colour in the CIELAB space. The feasibility of using CVS for jadeite-jade colour measurement was verified by an expert group test and a reasonable regression model in an experiment involving 111 samples covering almost all jadeite-jade colours. In the expert group test, more than 93.33% of CVS images are considered to have high similarities with real objects. Comparing L*, a*, b*, C*, h, and ∆E* (greater than 10) from CVS and CM tests indicate that significant visual differences exist between the measured colours. For a*, b*, and h, the R2 of the regression model for CVS and CM was 90.2% or more. CVS readings can be used to predict the colour value measured by CM, which means that CVS technology can become a practical tool to detect the colour of jadeite-jade.

Colour assessment of milk and milk products using computer vision system and colorimeter

2021 ◽  
pp. 105084
Author(s):  
Bojana Milovanovic ◽  
Ilija Djekic ◽  
Jelena Miocinovic ◽  
Bartosz G. Solowiej ◽  
Jose M. Lorenzo ◽  
...  
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Milk Products

scholarly journals The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Supakorn Harnsoongnoen ◽  
Nuananong Jaroensuk
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Measuring System ◽  
Coefficient Of Determination ◽  
Water Displacement ◽  
Non Invasive ◽  
Future Potential ◽  
Non Destructive ◽  
Egg Freshness

AbstractThe water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real—time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R2) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry.

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

scholarly journals Task Planner for Robotic Disassembly of Electric Vehicle Battery Pack

Metals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 387
Author(s):  
Martin Choux ◽  
Eduard Marti Bigorra ◽  
Ilya Tyapin
Keyword(s):  
Computer Vision ◽  
Electric Vehicles ◽  
Measurement Errors ◽  
Vision System ◽  
Lithium Ion ◽  
Battery Pack ◽  
Computer Vision System ◽  
Electric Vehicle Battery ◽  
Robotic Disassembly ◽  
Total Average

The rapidly growing deployment of Electric Vehicles (EV) put strong demands on the development of Lithium-Ion Batteries (LIBs) but also into its dismantling process, a necessary step for circular economy. The aim of this study is therefore to develop an autonomous task planner for the dismantling of EV Lithium-Ion Battery pack to a module level through the design and implementation of a computer vision system. This research contributes to moving closer towards fully automated EV battery robotic dismantling, an inevitable step for a sustainable world transition to an electric economy. For the proposed task planner the main functions consist in identifying LIB components and their locations, in creating a feasible dismantling plan, and lastly in moving the robot to the detected dismantling positions. Results show that the proposed method has measurement errors lower than 5 mm. In addition, the system is able to perform all the steps in the order and with a total average time of 34 s. The computer vision, robotics and battery disassembly have been successfully unified, resulting in a designed and tested task planner well suited for product with large variations and uncertainties.

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