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WHDL - 00011925
Submitted to the Department of Mathematics and Computer Science in partial fulfillment of the requirements for the degree of Bachelor of Arts & Bachelor of ScienceThis project set out to use aerial imagery from Small Unmanned Aircraft Systems (sUAS) to train a Region Convolutional Neural Network (RCNN) to identify and label linear features. For this research, significant amounts of training data were generated using labelImg for rectangular object identification and labelMe for polygonal object detection. This training data was then used to retrain a RCNN to identify and label rail grades, mine tailings, hand stacks, dirt roads, and foundations. Several pre-trained models, including: ssd_mobilenet_v1_coco, faster_rcnn_inception_v2_coco, and rfcn_resnet101_coco were used as a starting point for retraining. Each of these models was designed to allow further retraining of the RCNN, however, each one had roadblocks that prevented successful retraining in this experiment. Several roadblocks were identified that caused valuable time to be wasted. Google Drive proved to be troublesome when attempting to move large amounts of data necessary for retraining. This led to valuable time being spent attempting to send data to and from Google’s server that could have been spent further diagnosing retraining errors. To counteract this, an API was developed that would allow for training imagery to be stored easily on the NNU servers rather than Google Drive.