Multi-view Inspection of Flare Stacks Operation Using a Vision-controlled Autonomous UAV

Flare stacks are crucial safety control components in petrochemical plants that required efficient monitoring and inspection. In this work, an Unmanned Aerial Vehicle (UAV)-based multi-view operation inspection system for monitoring and assessing the operation of flare stacks is proposed. Image-Based Visual Servoing (IBVS) control is used to guide the autonomous UAV for multi-view visual data collection. Afterwards, the collected visual data is analyzed using a new Multi-View Convolutional Neural Network (MV-CNN) deep learning model to obtain useful conclusions on the system's operation and classify the current state of the observed system. The proposed system's performance was validated in a simulated petrochemical plant environment with operational flare stacks and the results showed superior performance of the proposed MV-CNN model compared to a conventional single-view CNN model.