Improved Aircraft Recognition for Aerial Refueling through Data Augmentation in Convolutional Neural Networks

Document Type

Conference Proceeding

Publication Date



As machine learning techniques increase in complexity, their hunger for more training data is ever-growing. Deep learning for image recognition is no exception. In some domains, training images are expensive or difficult to collect. When training image availability is limited, researchers naturally turn to synthetic methods of generating new imagery for training. We evaluate several methods of training data augmentation in the context of improving performance of a Convolutional Neural Network (CNN) in the domain of fine-grain aircraft classification. We conclude that randomly scaling training imagery significantly improves performance. Also, we find that drawing random occlusions on top of training images confers a similar improvement in our problem domain. Further, we find that these two effects seem to be approximately additive, with our results demonstrating a 45.7% reduction in test error over basic horizontal flipping and cropping.


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Source Publication

Advances in Visual Computing. ISVC 2016 (LNCS 10072)