论文标题
空中图像中车辆检测的生成数据增强
Generative Data Augmentation for Vehicle Detection in Aerial Images
论文作者
论文摘要
培训数据的稀缺是需要大量数据的深网之一。数据增强是一种广泛使用的方法，可以增加训练样本的数量及其变化。在本文中，我们专注于改善航空图像中的车辆检测性能，并提出一种生成的增强方法，该方法不需要比训练数据集中车辆对象的边界框注释了任何额外的监督。提出的方法通过允许探测器接受更高数量的实例训练，尤其是在训练实例数量有限的情况下，可以提高车辆检测的性能。提出的方法是通用的，因为它可以与不同的发电机集成。实验表明，该方法分别与多元化和深填充相结合后，该方法将平均精度提高了25.2％和25.7％。
Scarcity of training data is one of the prominent problems for deep networks which require large amounts data. Data augmentation is a widely used method to increase the number of training samples and their variations. In this paper, we focus on improving vehicle detection performance in aerial images and propose a generative augmentation method which does not need any extra supervision than the bounding box annotations of the vehicle objects in the training dataset. The proposed method increases the performance of vehicle detection by allowing detectors to be trained with higher number of instances, especially when there are limited number of training instances. The proposed method is generic in the sense that it can be integrated with different generators. The experiments show that the method increases the Average Precision by up to 25.2% and 25.7% when integrated with Pluralistic and DeepFill respectively.
