Systems and methods for detecting and identifying explosives

An explosives detection and identification processing module comprising a control system suitable to receive and process the image data transmitted from an unmanned aerial vehicle (UAV), and wherein the module comprises a machine-readable medium having instructions stored thereon for execution by a processor to perform a method comprising the steps of:

• storing the image data received from a UAV on the machine-readable medium;
• processing the image data to generate images of a training area with known explosives;
• fusing the images of the training area to generate an orthomosaic image representative of the training area with the explosives;
• analyzing one or more split images derived from the orthomosaic image using image processing algorithms, computer machine learning, computer vision machine learning (CVML), and/or an artificial neural network trained to detect patterns or predefined objects to generate a training model for automating the detection of explosives;
• identifying and labelling the explosives in the training area in the orthomosaic image to refine the training model;
• and
• applying the refined training model to a target area to predict the location of unknown explosives in the target area by comparing trained orthomosaic image data from the target area to previously generated orthomosaic image data from training area,
• such that the module is suitable for detecting and identifying explosives in the target area using the unmanned aerial vehicle (UAV).

A method for detecting and identifying explosives in a target area using an unmanned aerial vehicle (UAV), the method comprising the steps of:

• storing image data received from a UAV on a machine-readable medium;
• processing the image data to generate images of a training area with known explosives;
• fusing the images of the training area to generate an orthomosaic image representative of the training area with the explosives;
• analyzing one or more split images derived from the orthomosaic image using image processing algorithms, computer machine learning, computer vision machine learning (CVML), and/or an artificial neural network trained to detect patterns or predefined objects to generate a training model for automating the detection of explosives;
• identifying and labelling the explosives in the training area in the orthomosaic image to refine the training model;
• and
• applying the refined training model to a target area to predict the location of unknown explosives in the target area by comparing trained orthomosaic image data from the target area to previously generated orthomosaic image data from training area,
• such that the method is suitable for detecting and identifying explosives in the target area using the unmanned aerial vehicle (UAV).