Grant: Collaborative Research and Development Grants
Concordia University
$8,329.00
Computer aided tools for automation of industrial inspections_x000d_
EF 2020-21
Résumé
Infrastructel is an engineering consulting firm operating in Quebec. It employs over 160 people and is a leader in engineering and design of public utility infrastructure and outside telecommunications networks. In this project, we propose three different system to support the surveying of telecommunication civil works. The first is a continuation of our work with access wells. The current prototype has some limitations such as low-quality imagery and limited field of view. We propose to integrate to this system with a regular high-definition camera and increase its accuracy by developing new robust measuring algorithms. In the same time, we aim to build a database with the information regarding the access wells.
The second system focuses on terrain inspection for ducts installation. Keeping track of a terrain profile and its irregularities and the position of the new and existing ducts during construction is an essential task as the unenforced terrain can shift from day to day due to rain or wind. We propose a system that can take measurements on such a terrain and automatically during field inspection signal differences and assist the surveying engineer in preparing the report.
The third system focuses on utility poles. This is a very challenging task. We propose a deep learning solution where a neural network is trained to learn what a correct utility pole looks like and it detects anomalies based on a statistical distribution. A deep neural network, either in real-time or as a postprocessing step will detect the objects that are likely to have incorrect configurations and will signal them to an operator. For inaccessible areas, we will consider using drones for surveying. A database with the information from the existing poles will be created automatically.
Programme
Collaborative Research and Development Grants
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
535746-2018
Name nom
Popa, Tiberiu
Department département
Computer Science and Software Engineering
Organization id
70
Institution établissement
Concordia University
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2018
Award amount
8329
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
801
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Computer software
Area of application fr
Logiciel
Research subject code
2708
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Image processing
Research subject fr
Traitement des images
Installment
inst:2 - 2
Partie
1
Nb partie
2
Application title
Computer aided tools for automation of industrial inspections_x000d_
Keyword
NSERC
Application summary
Infrastructel is an engineering consulting firm operating in Quebec. It employs over 160 people and is a leader in engineering and design of public utility infrastructure and outside telecommunications networks. In this project, we propose three different system to support the surveying of telecommunication civil works. The first is a continuation of our work with access wells. The current prototype has some limitations such as low-quality imagery and limited field of view. We propose to integrate to this system with a regular high-definition camera and increase its accuracy by developing new robust measuring algorithms. In the same time, we aim to build a database with the information regarding the access wells.
The second system focuses on terrain inspection for ducts installation. Keeping track of a terrain profile and its irregularities and the position of the new and existing ducts during construction is an essential task as the unenforced terrain can shift from day to day due to rain or wind. We propose a system that can take measurements on such a terrain and automatically during field inspection signal differences and assist the surveying engineer in preparing the report.
The third system focuses on utility poles. This is a very challenging task. We propose a deep learning solution where a neural network is trained to learn what a correct utility pole looks like and it detects anomalies based on a statistical distribution. A deep neural network, either in real-time or as a postprocessing step will detect the objects that are likely to have incorrect configurations and will signal them to an operator. For inaccessible areas, we will consider using drones for surveying. A database with the information from the existing poles will be created automatically.