Systems and methods to determine object position using images captured from mobile image collection vehicle

A system, comprising:

• an aerial vehicle including:
• a camera;
• a first processor;
• and
• a first memory that stores first computer instructions that, when executed by the first processor, cause the first processor to:
• control movement of the aerial vehicle over a geographical area;
• capture, via the camera, a first image of the geographical area;
• and
• record a capture height of the aerial vehicle and avionic telemetry information of the aerial vehicle when the first image is captured;
• a server including:
• a second processor;
• and
• a second memory that stores second computer instructions that, when executed by the second processor, cause the second processor to:
• obtain the first image and the avionic telemetry information;
• perform a homography transform on the first image to generate a second image having uniform-pixel-distances per ground unit based on the capture height and the avionic telemetry information;
• perform image recognition on the second image to identify an object in the second image using an artificial neural network trained on a dataset of trained object parameters, wherein the second image is divided into a plurality of tiles, wherein each of the plurality of tiles is input into an artificial neural network trained on trained object parameters, and wherein a bounding box is generated for the object based on results from the artificial neural network;
• determine a first pixel location of the object within the second image based on the bounding box;
• perform a reverse homography transform on the first pixel location to determine a second pixel location in the first image for the object;
• determine a position of the object within the geographical area based on the second pixel location within the first image and the capture height and the avionic telemetry information;
• and
• store the determined position of the object.

An object-detection computing device, comprising:

• a memory that stores second computer instructions;
• and
• a processor that is configured to execute the computer instructions to:
• obtain a first image of a geographic area;
• perform a homography transform on the first image to generate a second image having a uniform-pixel-distance per ground unit based on an image height and avionic telemetry information associated with an aerial vehicle that captured the first image;
• perform image recognition on the second image to identify an object in the second image using an artificial neural network trained on a dataset of trained object parameters, wherein the second image is divided into a plurality of tiles, wherein each of the plurality of tiles is input into an artificial neural network trained on trained object parameters, and wherein a bounding box is generated for the object based on results from the artificial neural network;
• determine a first pixel location of the object within the second image based on the bounding box or segmented search zones that are searched by the trained artificial neural network;
• perform a reverse homography transform on the first pixel location to determine a second pixel location in the first image;
• determine a position of the object within the geographical area based on the second pixel location within the first image and the image height and the avionic telemetry information;
• and
• store the determined position of the object.