Video Analysis of Flare Stacks with an Autonomous Low-Cost Aerial System

Objectives/Scope: The inspection of flare stacks operation is a challenging task that requires time and human effort. Flare stack systems undergo various types of faults, including cracks in the flare stack's structure and incomplete combustion of the flared gas, which need to be monitored in a timely manner to avoid costly and dangerous accidents. Automating this inspection process via the application of autonomous robotic systems is a promising solution for minimizing the involved hazards and costs. Methods, Procedures, Process: In this work, we present an autonomous low-cost aerial system to be used as a flare stack inspection system. The proposed UAV system uses the visual signal obtained from an on-board camera for analyzing the observed scene, guiding the UAV's movement, and obtaining spectral data measurements from the flare during operation of the inspected system. The UAV system uses a deep learning detection network for detecting the flare stack's structure and extracting visual features. These visual features are used simultaneously for guiding the UAV's movement along the structure inspection mission and computing combustion-related measures. Results, Observations, Conclusions: The deep learning network was trained for inspecting the structure and monitoring the operation of the flare stack system. Simulations were conducted for evaluating the performance of the proposed structure and operation inspection technique and real images of flare stacks were used for testing the initial phases of the prototype. The developed system could autonomously collect an image database of the flare stack's structure for inspection purposes. Moreover, the trained deep learning detector could accurately detect combustion-related objects, such as flame and smoke, to give a conclusion about the current state of the flare stack system. Novel/Additive Information: The current system introduces a novelty to combine 3D navigation using visual servoing and a deep learning detection network in an autonomous UAV system for automating the process of flare stacks inspection and monitoring. The implementation of such system is expected to lower the cost and minimize the human resource risks of flare stack inspection processes.