PANTOBOT: A Computer Vision System for the Automatic Inspection of Locomotive Pantographs

2012 ◽  
Author(s):  
M. Sacchi ◽  
L. Ascari ◽  
S. Cagnoni ◽  
A. Piazzi ◽  
D. Spagnoletti
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Automatic Inspection ◽  
Computer Vision System

Yarn Package Inspection with Computer Vision and Robotics

1988 ◽  
Vol 58 (10) ◽  
pp. 605-614 ◽  
Author(s):  
N. B. Emery ◽  
D. R. Buchanan
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Host Computer ◽  
Automatic Inspection ◽  
Computer Vision System ◽  
Robot Controller ◽  
Inspection Process ◽  
And Robotics

A system for the automatic inspection of open-end polyester/cotton blend yarn packages, including package transportation and inspection algorithms, has been developed and implemented. An American robot was used to move and position the yarn packages during the inspection procedure. The robot controller served as the host computer and directed the sequence of inspection routines. An IRI P256 computer vision system performed the inspection steps for eleven of the most common package flaws. Several hundred packages were inspected with this system and its accuracy was comparable to that of human inspectors. Although over 85% of the inspection cycle was spent in package manipulation, the computer vision portion of the inspection process was as fast as human inspectors.

Research on Detection of Micro Plastic Gear Tooth Based on Dummy Circle Scan Method

2010 ◽  
Vol 37-38 ◽  
pp. 1002-1005
Author(s):  
Zhen Xiang Zhang ◽  
Kun Wang ◽  
Xun Yang ◽  
Lian Qing Chen
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Gear Tooth ◽  
Location Theory ◽  
Automatic Inspection ◽  
Computer Vision System ◽  
Image Preprocessing ◽  
Pixel Location ◽  
Plastic Gears

The quality of micro plastic gears was inspected with the image recognition technology herein. Focusing on the situation that gears’ defects were uncertain, the construction of computer vision system and the theories, technologies of digital image acquisition, image preprocessing, image segmentation as well as the sub-pixel location theory were studied thoroughly. A dummy circle scan method is presented to realize the gear tooth inspection, and the results indicate that it can meet request on the automatic inspection of micro plastic gears.

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 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.

scholarly journals Evaluating the diameter of eyedropper tips using a computer vision system

2019 ◽  
Vol 82 (1) ◽  
Author(s):  
Edicley Vander Machado ◽  
Priscila Cardoso Cristovam ◽  
Denise de Freitas ◽  
José Álvaro Pereira Gomes ◽  
Vagner Rogério dos Santos
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System
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