@inproceedings{62d1a726a32945618821e16e9a22d93e,
title = "Multi-view Inspection of Flare Stacks Operation Using a Vision-controlled Autonomous UAV",
abstract = "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.",
keywords = "Flare stack, Multi-view Inspection, Unmanned Aerial Vehicle",
author = "\{Al Radi\}, Muaz and Pengfei Li and Hamad Karki and Naoufel Werghi and Sajid Javed and Jorge Dias",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 ; Conference date: 16-10-2023 Through 19-10-2023",
year = "2023",
doi = "10.1109/IECON51785.2023.10312722",
language = "British English",
series = "IECON Proceedings (Industrial Electronics Conference)",
publisher = "IEEE Computer Society",
booktitle = "IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society",
address = "United States",
}