This paper introduces a thermographic vision system to detect different types of hotspots on a variety of cable junctions commonly found in Hydro-Québec underground electrical distribution network. Cable junctions of underground distribution networks operate in harsh conditions, potentially leading to failure overtime. Faults can be prevented by the timely detection of local hotspot on these junctions. Hotspot detection is carried out by mean of image segmentation using a deep neural network. Special care is given to uncertainty estimation and validation. Uncertainty is used to assess the quality of a segmentation to avoid misdiagnosis or returning in the field to recapture images. It is also proposed as a tool to evaluate whether unannotated images should be included in the dataset. System performance has been evaluated on a test dataset as well as in the field by regular inspection teams. Promising results obtained so far led to the deployment of the vision system on a fleet of five inspection trucks performing inspection over the province over the last year Authorization was granted to scale the solution to 35 trucks starting this year.

This paper presents the design of a multirotor unmanned aerial vehicle (UAV) capable of landing semiautomatically on a power line while carrying a payload. The vehicle then rolls along the line to perform an inspection. Special attention is given to the vehicle's onboard vision system, which consists of a monocular camera and LiDAR used together to compute the pose of the vehicle relative to the power line. Landing assistance is provided to the pilot by a position-based visual controller that aligns and keeps the vehicle centered along the power line. The pilot remains in control of vertical and longitudinal movement during descent. The proposed approach was tested on a full-scale test line and shows promise for future applications of high value to the electric industry such as non-destructive testing of power transmission lines.