Elder, James H.Taheri-Shirazi, Maryam2020-11-132020-11-132020-102020-11-13http://hdl.handle.net/10315/37992Finding and rescuing people from downed aircraft is challenging in many parts of the world, including Canada. Because the Canadian military still relies on the naked eye to conduct searches, airborne search and rescue could benefit greatly from advanced sensor systems. Partial automation of target detection could alleviate operator workload and potentially improve rescue efforts. One of the obstacles to developing such a system has been the lack of a large, realistic, and ground-truthed search and rescue (SAR) dataset. I used a new dataset for airborne SAR collected in 2014 by the National Research Council Flight Research Laboratory (NRC-FRL) and labeled approximately 40,000 frames, to extract roughly 20,000 negative and 20,000 positive images. Then I tested three ATD methods on this dataset in order to develop more advanced assisted target detection algorithms for thermal infrared (IR) images.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Computer scienceElectronic Thesis or Dissertation2020-11-13Maryam Taheri-ShiraziSearch and Rescue DatasetMachine LearningDeep LearningObject Detection