@inproceedings{663dfa27039342679082f77d52a2fe10,
title = "Pipeline Leak Identification Emergency Robot Swarm (PLIERS)",
abstract = "As cities continue to expand, there has been a substantial increase in demand for factories and sewage systems, which in turn necessitated the need for maintaining the pipeline systems involved. These are typically difficult, dangerous, and expensive to investigate manually by humans, and therefore the use of automated robots is crucial to identifying cracks. Therefore, we propose PLIERS (Pipeline Leak Identification Emergency Robot Swarm). PLIERS is a system consisting of a swarm of robots that are used to investigate pipes for any cracks while communicating with one another as well as a server system. The robots are mounted with cameras to collect images of these cracks, which are then analyzed using a machine learning algorithm by the server to detect and confirm the cracks and their severity.",
keywords = "convolutional neural network, crack detection, pipelines, robots",
author = "Ayman Kandil and Ali Darwiche and Reem Qasem and Fatima Matook and Ahmad Younis and Habib Badran and Maryam Bin-Jassem and Ossama Ahmed and Ali Behiry and Abd, {Mohammed El} and Mounib Khanafer",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023 ; Conference date: 11-09-2023 Through 14-09-2023",
year = "2023",
month = sep,
doi = "10.1109/SECON58729.2023.10287512",
language = "English",
series = "Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops",
publisher = "IEEE Computer Society",
pages = "381--383",
booktitle = "2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023",
address = "United States",
}