Machine-learned explainable object detection system and method

An explained machine learning object detection system, comprising:

• an input module connected to sensors and a data server, wherein the input module receives data frames from the sensors corresponding to a first time and wherein the input module receives data corresponding to the sensors position and objects-of-interest detectable from the sensors position;
• a region-of-interest module connected to the input module, wherein the region-of-interest module determines, using object-of-interest data, regions-of-interest of the data frames;
• a machine-learned object detection module connected to the region-of-interest module, wherein the machine-learned object detection module uses the regions-of-interest of the data frames to detect objects in the regions of interest of the data frames;
• and
• an explainable detection module connected to the machine-learned detection module, wherein the explainable detection module uses the regions of interest of the data frames where objects have been detected to identify objects of interest using computer vision processing and outputting explained detected object data.

A method of detecting a way-side object comprising;

• receiving a sensor data frames corresponding to a first time from sensors mounted on a vehicle,
• receiving position data corresponding to the 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 the sensor data frame;
• 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;
• processing sensor data frames corresponding to a series of times to generate a series of explained object-of-interest data;
• validating an object detection using the series of explained object-of-interest data;
• and
• outputting explained object-of-interest data corresponding to the validated object detection to a navigation system of the vehicle.

A method of detecting a way-side object comprising;

• receiving sensor data frames corresponding to a first time from sensors mounted on a vehicle;
• 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 a 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.