Detection of unknown maneuverability hazards in low-altitude UAS color imagery using linear features
Jeffrey J. Dale, David Huangal, James Alex Hurt, Trevor M. Bajkowski, James M. Keller, Grant J. Scott, Stanton R. Price · 2020
Deep learning approaches have very quickly become the most popular framework for both semantic segmentation and object detection/recognition tasks. Especially in object detection, however, supervised models like deep neural networks are inherently prone to find only classes from the training data in the testing set. In domains where the safety and security of operators are entrusted to machine learning algorithms, it is often infeasible or impossible to train supervised models on all possible classes; thus, a supplementary unsupervised approach is needed. For the specific problem of detecting potential maneuverability hazards within road segmentation networks, we propose an unsupervised solution using linear features with a voting scheme at each pixel within a pre-supplied road segmentation map, yielding a consensus-based confidence of how unlike a pixel is to surrounding road pixels. This approach is verified on UAS imagery collected by the U.S. Army ERDC.