The unmanned aerial vehicles (UAVs) significantly contribute to the convenience and intelligence of life. However, the large use of UAVs also leads to high security risk. Only detecting the small and flying UAVs can prevent the safety accidents. UAV detection task could be regarded as a branch of object detection in flied of image processing. The advanced object detection models are mainly data driven, which depend on large-scale databases. The well-labeled datasets have proved to be of profound value for the effectiveness and accuracy in various object detection tasks. Thus, the first step of detecting UAVs is to build up a dataset of UAVs. In this study, we collect and release a dataset for UAV detection, called UAVData. To maintain the universality and robustness of the trained models, balloons and 6 types of UAVs are recorded in the dataset which totally consists of 13,803 well-labeled and recognizable images. We further conduct strong benchmarks using several advanced deep detection models, including faster R-CNN, SSD, YOLOv3. In addition, we utilize 4 different convolutional neural network models as the backbone models of these object detection methods to learn UAV-related features in images. By providing this dataset and baselines, we hope to gather researchers in both UAVs detection and machine learning field to advance toward the application.

无人机（UAV）极大地促进了生活的便利性和智能性。但是，无人机的大量使用也带来了很高的安全风险。仅检测小型飞行的无人机可以防止安全事故。在图像处理的过程中，无人机检测任务可以看作是物体检测的一个分支。高级对象检测模型主要是数据驱动的，它依赖于大型数据库。事实证明，标记良好的数据集对于各种物体检测任务的有效性和准确性具有深远的价值。因此，检测无人机的第一步是建立无人机数据集。在这项研究中，我们收集并发布了用于无人机检测的数据集，称为UAVData。为了保持训练模型的通用性和鲁棒性，数据集中记录了气球和6种类型的无人飞行器，总共包含13,803个标签清晰且可识别的图像。我们使用多种高级深度检测模型进一步建立了强大的基准，包括更快的R-CNN，SSD和YOLOv3。另外，我们利用4种不同的卷积神经网络模型作为这些对象检测方法的骨干模型，以学习图像中与无人机相关的特征。通过提供此数据集和基线，我们希望聚集无人机检测和机器学习领域的研究人员，以朝着该应用程序发展。我们利用4种不同的卷积神经网络模型作为这些对象检测方法的骨干模型，以学习图像中与无人机相关的特征。通过提供此数据集和基线，我们希望聚集无人机检测和机器学习领域的研究人员，以朝着该应用程序发展。我们利用4种不同的卷积神经网络模型作为这些对象检测方法的骨干模型，以学习图像中与无人机相关的特征。通过提供此数据集和基线，我们希望聚集无人机检测和机器学习领域的研究人员，以朝着该应用程序发展。