Computer aided tools for automation of industrial inspections**

** Prosstructel是一家在魁北克运营的工程咨询公司。它拥有160多名员工，是公用事业基础设施和外部电信网络工程和设计的领导者。在这个项目中，我们提出了三种不同的系统来支持电信土木工程的测量。第一个是继续我们的工作与访问威尔斯。目前的原型有一些局限性，如低质量的图像和有限的视野。我们建议将此系统与常规高清摄像机集成，并通过开发新的强大的测量算法来提高其精度。与此同时，我们的目标是建立一个数据库，其中包含有关访问威尔斯的信息。*第二个系统侧重于管道安装的地形检查。在施工过程中跟踪地形剖面及其不规则性以及新管道和现有管道的位置是一项重要任务，因为未加固的地形可能会因下雨或刮风而每天发生变化。我们提出了一个系统，可以在这样的地形测量，并在现场检查信号的差异，并协助测量工程师准备报告自动。* 第三个系统侧重于电线杆。这是一项非常具有挑战性的任务。我们提出了一种深度学习解决方案，其中训练神经网络以学习正确的电线杆是什么样子，并根据统计分布检测异常。深度神经网络，无论是实时的还是作为后处理步骤，都将检测到可能具有不正确配置的对象，并将其发送给操作员。对于交通不便的地区，我们会考虑使用无人机进行勘测。将自动创建一个包含现有杆信息的数据库。****

**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. ****