Savarese, Learning social etiquette: Human trajectory understanding in crowded scenes, in European Conference on Computer Vision, Springer, (2016), 549–565. Hu, Vision meets drones: A challenge, preprint, arXiv: 1804.07437.Ī. Li, et al., The unmanned aerial vehicle benchmark: Object detection and tracking, in European Conference on Computer Vision, Springer, (2018), 375–391. Ramanan, et al., Microsoft coco: Common objects in context, in European Conference on Computer Vision, Springer, (2014), 740–755.ĭ. Fei, ImageNet: A large–scale hierarchical image database, in 2009 IEEE Conference on Computer Vision and Pattern Recognition, (2009), 248–255. Zisserman, The pascal visual object classes (voc) challenge, Int. Zhu, Embedded system for road damage detection by deep convolutional neural network, Math. Dorado, et al., Estimating tree height and biomass of a poplar plantation with image-based UAV technology, AIMS Agric. How to construct low-altitude aerial image datasets for deep learning. In addition, in order to make up the shortage of data, we introduce data augmentation techniques, including traditional data augmentation and data augmentation based on oversampling and generative adversarial networks.Ĭitation: Xin Shu, Xin Cheng, Shubin Xu, Yunfang Chen, Tinghuai Ma, Wei Zhang. Then, we recommend several commonly used image annotation tools and crowdsourcing platforms for data annotation to generate labeled data for model training. On this basis, we put forward some suggestions on data collection of low-altitude aerial images. Firstly, we introduce the existing low-altitude aerial images datasets and analyze the characteristics of low-altitude aerial images. In this paper, we take low-altitude aerial image object detection as an example to propose a framework to demonstrate how to construct datasets for specific tasks. Since the commonly used datasets are not designed for specific scenarios, in order to give UAVs stronger computer vision capabilities, large enough aerial image datasets are needed to be collected to meet the training requirements. Computer vision tasks based on deep learning usually require a large amount of task-related data to train algorithms for specific tasks. The combination of Unmanned Aerial Vehicle (UAV) technologies and computer vision makes UAV applications more and more popular.
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