불확실성을 이용한 딥러닝 기반의 항공 이미지 객체 탐지

초록

Object detection in aerial images is an important task because it is used in various applications such as land management, disaster monitoring, national security, and map production. However, owing to the characteristics of aerial images, such as high resolution, data imbalance between classes, lack of data, and densely appearing objects, it is difficult to improve the performance even with the recent deep learning-based object detection models. To overcome these challenges, this paper proposes an uncertainty-based max-margin learning method and a data augmentation method based on attribute transformation specialized for aerial images. The superiority of the proposed methods based on a deep learning-based object detection model is revealed by it winning the aerial image object detection contest 2020

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