Vision-based Inspection of Flare Stacks Operation Using a Visual Servoing Controlled Autonomous Unmanned Aerial Vehicle (UAV)

The inspection of flare stacks’ operation is a challenging task that requires technical expertise and human effort. Flare stack systems undergo various types of faults that need to be monitored in a timely manner to avoid costly and dangerous accidents. Automating this process via the application of autonomous robotic systems for collecting comprehensive data of the flare stack’s operation is a promising solution for minimizing the involved hazards and costs. In this work, a novel Unmanned Aerial Vehicle (UAV)-based autonomous inspection system for flare stacks performance monitoring is proposed. The system employs a deep learning detection network that was trained for detection of flame and smoke for vision-based flaring performance analysis. A visual servoing control technique was used for guiding the UAV’s movement throughout the inspection mission for collecting comprehensive visual inspection data. Simulations in a simulated petrochemical plant environment with flare stacks were performed for validating the performance of the proposed system. The proposed UAV system was able to collect the required data successfully and analysis of the obtained data returned useful information about the flare stack’s operation.