US 12,217,330 B2
Method and device for generating learning data for an artificial intelligence machine for aircraft landing assistance
Thierry Ganille, Mérignac (FR); Guillaume Pabia, Mérignac (FR); and Christian Nouvel, Mérignac (FR)
Assigned to THALES, Courbevoie (FR)
Appl. No. 17/774,807
Filed by THALES, Courbevoie (FR)
PCT Filed Nov. 3, 2020, PCT No. PCT/EP2020/080803
§ 371(c)(1), (2) Date May 5, 2022,
PCT Pub. No. WO2021/089536, PCT Pub. Date May 14, 2021.
Claims priority of application No. 1912484 (FR), filed on Nov. 7, 2019.
Prior Publication US 2022/0406040 A1, Dec. 22, 2022
Int. Cl. G06T 11/00 (2006.01); G06T 5/50 (2006.01); G06V 10/44 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01)
CPC G06T 11/00 (2013.01) [G06T 5/50 (2013.01); G06V 10/454 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/176 (2022.01); G06T 2207/10048 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating labeled training data able to be used by an artificial intelligence machine implementing a deep learning image processing algorithm, the method comprising at least the steps of:
defining parameters for a scenario of an aircraft approaching a runway;
using the parameters of the scenario in a flight simulator to generate simulated flight data, said flight simulator being configured so as to simulate said aircraft in the approach phase and an associated autopilot;
using the simulated flight data in a sensor simulator to generate simulated sensor data, said sensor simulator being configured so as to simulate a forward-facing sensor on board an aircraft and able to provide sensor data representative of information of interest of a runway, said simulated sensor data that are generated being representative of information of interest of said runway;
using the simulated flight data and the simulated sensor data to generate a ground truth, said ground truth associated with the simulated sensor data forming a pair of simulated labeled training data; and
wherein the step of generating the ground truth consists in:
generating a first-level ground truth comprising labeling data representative of characteristics of the simulated runway, without a visibility restriction;
detecting the visibility limit by identifying areas without visibility in the simulated sensor data; and
correcting the first-level ground truth with the identified areas without visibility in order to generate said ground truth.