The ambition of SmaCS is to develop an automated system that will incorporate AI-based technology for digitalized on-demand verification of Taxi, Take-off and Landing (TTL) requirements for cabin luggage to help the crew in handling safety procedures. The system will particularly address the optimal installation in aircrafts, paying special attention to important aspects such as the responsiveness, robustness, power consumption, maintenance, scalability, upgradeability, compatibility with the international aviation regulations, adaptability to any kind of cameras already installed on a plane, etc. Furthermore, in order to reduce the total cost of the system the relation between number of cameras and processing modules will be maximized. 
In SmaCS the data used for training the AI-based technology will optimally combine real and synthetic annotated images with a high variety of situations and visual appearances of passengers, luggage and cabin components. Sophisticated synthetic-to-real domain adaptation techniques will be developed to generate data of better quality specifically designed for the considered scenarios, overcoming the lack of suitable data to train a tailored image content descriptor.

This work has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 865162.

Project Website: https://www.smacs.eu/