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.
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Zeng, Y., Duan, Q., Chen, X. et al. UAVData: A dataset for unmanned aerial vehicle detection. Soft Comput (2021). https://doi.org/10.1007/s00500-020-05537-9
- Object detection
- UAV detection
- UAV image dataset
- Convolutional neural network