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21 April 2020 Evaluating deep road segmentation techniques for low-altitude UAS imagery
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Abstract
Semantic segmentation, the task of assigning a class label to each pixel within a given image, has applications in a wide variety of domains, ranging from medicine to self-driving vehicles. One successful deep neural network model that has been developed for semantic segmentation tasks is the U-Net architecture, a "U"-shaped neural network initially applied to segmentation of cell membranes in biomedical images. Additional variants of the U- Net have been developed within the research literature that incorporate new features such as residual layers and attention mechanisms. In this research, we evaluate various U-Net-based architectures on the task of segmenting the road and non-road in low-altitude UAS visible spectrum imagery. We show that these models can successfully extract the roads, detail a variety of performance metrics of the respective networks' segmentations, and show examples of successes and pending challenges using U.S. Army ERDC imagery collected from a variety of ight routes and altitudes in a complex environment.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David Huangal, Jeffrey Dale, J. Alex Hurt, Trevor M. Bajkowski, James M. Keller, Grant J. Scott, and Stanton R. Price "Evaluating deep road segmentation techniques for low-altitude UAS imagery", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114131L (21 April 2020); https://doi.org/10.1117/12.2557610
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