Machine learning based automated object recognition for unmanned autonomous vehicles

A system comprising:

• a platform positioned within an underwater environment, the platform including a sonar capture system connected to a controller, the controller having a neural network implemented therein, the neural network trained to associate one or more visual features within the underwater environment with a target object of interest utilizing a known set of input data examples and labels using the following operations:
• 1) Save a target example sub-image from input located around an object coordinates;
• 2) Select other sub-images from the input that do not contain the object coordinates;
• 3) Save the other sub-images as not-target sub-images;
• 4) Organize a dataset to label the target example sub-image with a target label and the not-target sub-images with a not-target label;
• wherein the controller is configured to cause the sonar capture system to capture one or more input images from the underwater environment;
• wherein the controller is configured to use calculated output values of at least one convolutional filter of the neural network to recognize features of one or more of the input images that at least partially match one or more of the visual features within the underwater environment with the target object of interest;
• wherein the controller is configured to use a classifier of the neural network to calculate a label vector for the one or more input images where the label vector is a confidence value associated with the target object of interest;
• wherein the controller is configured to label, using the confidence value from the classifier of the neural network, the one or more input images that at least partially match the one or more visual features within the underwater environment that at least partially match the target object of interest;
• wherein the controller is configured to locate, using a combined label vector and class activation map output of the neural network, pixels within the one or more input images that at least partially match the one or more visual features within the underwater environment that at least partially match the target object of interest;
• and
• wherein the controller is configured to locate a geospatial position of the target object of interest based upon the pixels within the one or more input images.