Project description
Smarter cabin solutions take flight
The aviation industry needs new cost-effective and reliable solutions to increase security and competitiveness. The sector is subject to new regulations such as the TTL (ticket time limit) requirements for cabin luggage. The EU-funded SmaCS project, led by two key players in aeronautic camera systems and machine learning, OTOMY Aviation and VICOMTECH, proposes an advanced solution for digitalised verification of cabin luggage. The project will develop a machine learning algorithm for cabin luggage control in poor light and contrast conditions. It will also establish an advanced method to make data-driven predictions. The project is also working on an aeroplane compatible image data processing hardware. Overall, this new technology will promote safety-related solutions in the aviation and transportation sectors.
Objective
The SMACS project, led by OTONOMY Aviation and VICOMTECH, aspires to conceive a camera-based prototype solution for digitalized on-demand verification of TTL requirements for cabin luggage. It will be designed to be highly reliable, cost effective and easy to upgrade, with potential additional camera-based verification services.
To fulfil this ambition, the consortium will capitalise on 3 main pillars:
- A robust Machine Learning algorithm for cabin luggage recognition in low light and low contrast environment that will be built from VICOMTECH’s AI libraries and specific developments
- A highly innovative way to produce learning dataset based on videos coupled with synthetic 3D models
- An aircraft compliant, ultra-light, ultra-compact Image data processing hardware based on COTS, with highly adaptable CVMS interface connection capabilities.
VICOMTECH brings to the consortium its large experience and proven competencies in developing machine-learning based algorithms, notably for object recognition, and its rare and precious capacity to train algorithms from synthetic 3D models to reach higher performances at unmatchable costs.
OTONOMY Aviation brings to the consortium its deep knowledge of aeronautic compliant camera systems and camera implementation in aircraft cabins. Moreover, OTONOMY’s strong relationship with major actors of the ecosystem such as Airbus Interiors Services permitted to get a highly realistic synthetic model of an A320 aircraft that will be used for the algorithm training.
The SMACS project will generate new technology breakthrough for the use of IA in aeronautic and will bring new safety-related solutions to other sectors such as public transportations (trains, buses etc.).
It will also enable OTONOMY to introduce smart IA-empowered cameras in the aeronautic ecosystem, opening new and various opportunities for aeronautic competitiveness, safety and ecology. The derived products will generate an additional turnover of 10M$/year.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsoptical sensors
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringaircraft
- social sciencessocial geographytransportpublic transport
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencesdata sciencedata processing
Programme(s)
Funding Scheme
IA - Innovation actionCoordinator
33700 Merignac
France