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Authors: Luis Unzueta 1 ; Sandra Garcia 2 ; Jorge Garcia 1 ; Valentin Corbin 2 ; Nerea Aranjuelo 1 ; Unai Elordi 1 ; Oihana Otaegui 1 and Maxime Danielli 2

Affiliations: 1 Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain ; 2 Otonomy Aviation, 16 Avenue Pythagore, 33700 Mérignac, France

Keyword(s): Intelligent Sensors, Computer Vision, Machine Learning, Deep Neural Networks, Aircraft Cabin.

Abstract: Currently, aircraft cabin operations such as the verification of taxi, take-off, and landing (TTL) cabin readiness are done manually. This results in an increased workload for the crew, operational inefficiencies, and a non-negligible risk of human errors in handling safety-related procedures. For TTL, specific cabin readiness requirements apply to the passenger, to the position of seat components and cabin luggage. The usage of cameras and vision-based object-recognition algorithms may offer a promising solution for specific functionalities such as cabin luggage detection. However, building a suitable camera-based smart sensing system for this purpose brings many challenges as it needs to be low weight, with competitive cost and robust recognition capabilities on individual seat level, complying with stringent constraints related to airworthiness certification. This position paper analyzes and discusses the main technological factors that system designers should consider for buildin g such an intelligent system. These include the sensor setup, system training, the selection of appropriate camera sensors and lenses, AI-processors, and software tools for optimal image acquisition and image content analysis with Deep Neural Network (DNN)-based recognition methods. Preliminary tests with pre-trained generalist DNN-based object detection models are also analyzed to assist with the training and deployment of the recognition methods. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Unzueta, L.; Garcia, S.; Garcia, J.; Corbin, V.; Aranjuelo, N.; Elordi, U.; Otaegui, O. and Danielli, M. (2020). Building a Camera-based Smart Sensing System for Digitalized On-demand Aircraft Cabin Readiness Verification. In Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS, ISBN 978-989-758-479-4, pages 98-105. DOI: 10.5220/0010128500980105

@conference{robovis20,
author={Luis Unzueta. and Sandra Garcia. and Jorge Garcia. and Valentin Corbin. and Nerea Aranjuelo. and Unai Elordi. and Oihana Otaegui. and Maxime Danielli.},
title={Building a Camera-based Smart Sensing System for Digitalized On-demand Aircraft Cabin Readiness Verification},
booktitle={Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS,},
year={2020},
pages={98-105},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010128500980105},
isbn={978-989-758-479-4},
}

TY - CONF

JO - Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS,
TI - Building a Camera-based Smart Sensing System for Digitalized On-demand Aircraft Cabin Readiness Verification
SN - 978-989-758-479-4
AU - Unzueta, L.
AU - Garcia, S.
AU - Garcia, J.
AU - Corbin, V.
AU - Aranjuelo, N.
AU - Elordi, U.
AU - Otaegui, O.
AU - Danielli, M.
PY - 2020
SP - 98
EP - 105
DO - 10.5220/0010128500980105

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