Development of a Specialized Method for Contextual Augmentation of Small Objects

In the paper, existing methods of data augmentation have been analyzed and their shortcomings associated with limitations when operating with images obtained using unmanned aerial vehicles have been identified. A new specialized method for contextual augmentation of small objects, which supports spatial realism and is capable of providing the required density of small objects at the training stage of neural network models, has been proposed. Given the complexity of implementing the proposed method of contextual augmentation of small objects, it has been decided to divide the development process into several stages. To test empirically the proposed method, the Single Shot Detector MobileNet V2 model located in the TensorFlow 2 Detection Model Zoo repository has been selected as the base detector. This neural network model has been trained on the VisDrone dataset using standard data augmentation methods and the developed method. The obtained detection results of the basic neural network model have been compared with the improved model in accordance with the evaluation protocol of the Common Objects in Context Evaluation metrics library. Experimental studies have shown that context-aware augmentation significantly improves the ability of neural network models to detect small objects. The obtained result is of great practical importance when operating with compact energy-efficient systems with limited performance and memory.