US 12,223,738 B2
Machine-learned explainable object detection system and method
Veronica Marin, Toronto (CA); Evgeny Nuger, Toronto (CA); Jaewook Jung, Toronto (CA); William Wang, Toronto (CA); and David Beach, Toronto (CA)
Assigned to Hitachi Rail GTS Canada Inc., Toronto (CA)
Filed by Hitachi Rail GTS Canada Inc., Toronto (CA)
Filed on Feb. 1, 2022, as Appl. No. 17/590,264.
Claims priority of provisional application 63/144,251, filed on Feb. 1, 2021.
Prior Publication US 2022/0245949 A1, Aug. 4, 2022
Int. Cl. G06V 20/58 (2022.01); G06V 10/25 (2022.01); G06V 10/82 (2022.01)
CPC G06V 20/58 (2022.01) [G06V 10/25 (2022.01); G06V 10/82 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method of detecting a way-side object comprising;
receiving sensor data frames corresponding to a first time from sensors mounted on a vehicle,
receiving position data corresponding to a first position of the vehicle at the first time;
retrieving object-of-interest data from a database, wherein the object-of-interest data corresponds to an expected object-of-interest detectable by the sensors at the first position of the vehicle;
processing the sensor data frames and the object-of-interest data to determine a region-of-interest in at least one frame of the sensor data frames;
processing a portion of the sensor data frames corresponding to the region of interest using machine-learned object detection to identify a first object-of-interest;
processing the portion of the sensor data frames corresponding to the region of interest using computer vision methods to detect features of the expected object-of-interest and identifying the detected object-of-interest as explained when the features of the expected object-of-interest are detected; and
outputting explained object-of-interest data corresponding to the explained detected object-of-interest to a navigation system of the vehicle.