Computer aided tools for automation of industrial inspections

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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.