Abstract
Vehicle detection based on very high-resolution (VHR) remote sensing images is beneficial in many fields such as military surveillance, traffic control, and social/economic studies. However, intricate details about the vehicle and the surrounding background provided by VHR images require sophisticated analysis based on massive data samples, though the number of reliable labeled training data is limited. In practice, data augmentation is often leveraged to solve this conflict. The traditional data augmentation strategy uses a combination of rotation, scaling, and flipping transformations, etc., and has limited capabilities in capturing the essence of feature distribution and proving data diversity. In this study, we propose a learning method named Vehicle Synthesis Generative Adversarial Networks (VS-GANs) to generate annotated vehicles from remote sensing images. The proposed framework has one generator and two discriminators, which try to synthesize realistic vehicles and learn the background context simultaneously. The method can quickly generate high-quality annotated vehicle data samples and greatly helps in the training of vehicle detectors. Experimental results show that the proposed framework can synthesize vehicles and their background images with variations and different levels of details. Compared with traditional data augmentation methods, the proposed method significantly improves the generalization capability of vehicle detectors. Finally, the contribution of VS-GANs to vehicle detection in VHR remote sensing images was proved in experiments conducted on UCAS-AOD and NWPU VHR-10 datasets using up-to-date target detection frameworks.
Highlights
Fast and robust vehicle detection in remote sensing images has potential applications in traffic surveillance, emergency management, and economic analysis
The contribution of Vehicle Synthesis Generative Adversarial Networks (VS-Generative Adversarial Nets (GANs)) to vehicle detection in very high-resolution (VHR) remote sensing images was proved in experiments conducted on UCAS-AOD and NWPU VHR-10 datasets using up-to-date target detection frameworks
VS-GANs were compared with those derived from other data augmentation methods
Summary
Fast and robust vehicle detection in remote sensing images has potential applications in traffic surveillance, emergency management, and economic analysis. The convolutional neural network (CNN) has been applied to aerial image object detection, and it achieved promising results. The performance of CNN-based vehicle detectors heavily dependent on the quality and quantity of annotations of the training data. In a GAN, the generator and classifier networks are jointly learned based on the observed training dataset, and the observed and synthesized data samples are combined to train the detector network. Networks (VS-GANs) is proposed, which utilizes GANs to generate labeled data samples for the vehicle detection task (Figure 2). Networks (VS-GANs) is proposed, which utilizes GANs to generate labeled data samples for the detection task — The data generated by our model can be combined with real datasets to train CNN-based.
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