Vehicle detection on small scale data by generative data augmentation

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2021-2-03
Kumdakcı, Hilmi
Scarcity of training data is one of the prominent problems for deep neural networks,which commonly require high amounts of data to display their potential. Data aug-mentation techniques are frequently applied during the pre-training and training phasesof deep neural networks to overcome the problem of having insufficient data for train-ing. These techniques aim to increase a neural network’s generalization performanceon unseen data by increasing the number of training samples and provide a more rep-resentative distribution to the system during training. In this work, we focus on im-proving vehicle detection in aerial images by proposing a data augmentation methodthat does not need any extra supervision than the bounding box annotations of thevehicle instances in the training data. The methods we used are based on a condi-tional Generative Adversarial Network (cGAN). The proposed method is not exclu-sive and can be used in association with classical augmentation techniques to furtherimprove object detection performance. We showed that the proposed data augmenta-tion method increases the Average Precision by up to 25.2%, 32.7%, and 25.7% whenintegrated with Pluralistic, PSGAN, and DeepFill respectively.

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Citation Formats
H. Kumdakcı, “Vehicle detection on small scale data by generative data augmentation,” M.S. - Master of Science, Middle East Technical University, 2021.