Model-Aided Drone Classification Using Convolutional Neural Networks

Classifiers using convolutional neural networks (CNNs) often yield high accuracies on samples that come from the same distribution as the training data. In this study we evaluate a CNN classifier's ability to discriminate drones from non-drone targets, such as birds, when they are not represented in the training data. We found that the mean accuracy on such out-of-distribution drones was 78%. By introducing a synthetic drone class, generated from a mathematical model, the out-of-distribution drone accuracy was improved to 86%. When trained on all drone types the mean accuracy over all classes was 90%. The data was collected with a 77 GHz mechanically scanning radar with only 9 ms dwell time.