Gun Weapons Systems Synthetic Unmanned Aerial Systems Imagery Data Set

The purpose of this research is to enhance a synthetic imagery dataset of Unmanned Aerial Systems (UAS) using machine learning (ML) for computer vision discriminator applications. Unmanned Aerial Systems (UAS) pose a threat to the US Navy (USN) surface fleet. Counter-UAS results in successful negation of UAS threats by USN effectors. It requires the ability to detect, identify, discriminate, and engage in a cost-effective manner. In order to increase the automation of surface sensorsÆ ability to detect, identify, and discriminate UAS, large data sets of image and video data must be collected. The number and variety of UAS and the need for all aspect coverage make physical data collections costly in terms of time and money. An automated visual synthetic data generation using ML to develop these large data sets would be advantageous. The produced data sets will be used to train algorithms. The proposed solution provides data as seen at a nose-on view, top-down aerial view, and broad side view (i.e., plan, profile, and various oblique angles). The proposed solutionĀ will demonstrate realism of the dataset through analysis and modeling. The solution will contain a synthetic dataset of frame-by-frame UAS images (not video sequences) in both visible and thermal bands that is useful for application to the training, validation, and testing of ML and artificially intelligent sub-systems for Naval Gunnery Systems. The solution produces a dataset that conforms to commonly available public standards and contains images and labels of ground truth objects in accordance with class ontology, such as the Jet Propulsion Laboratory Semantic Web for Earth and Environmental Terminology (SWEET). The dataset will contain at least 3 types of group 1 UAS and at least 2 types of group 2 UAS. The synthetically generated data will be photo-realistic for both the visible and thermal imagery with high definition (HD) resolution. The labels will be at rectangular labels, with segmentation labels being the objective. The dataset will have diverse object and scene composition with variations in object size, orientation, background, lighting, and atmospheric conditions. The perspective and size of observation will also vary ranging between 2 pixels in the smallest dimension up to the full size of the image frames. The dataset will also be appropriately partitioned by the band of synthetic imagery (visible and thermal). It will also follow image dataset convention in the split of training for the training of ML systems; validation for the initial testing of the algorithm performance; and test for model performance verification with distinct data from the other sets. Each band of data will contain these three sub-sets.