# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### Datasets and Metrics for Vision-Based Detection
#### 2.4.2 Object Detection Dataset for UAVs  
The main image-based UAV datasets are the Real World dataset [74], the Det-Fly dataset [75], the MIDGARD dataset [76], the USC-Drone dataset [77], and the DUT-Anti-UAV [78]. These 5 datasets contain 56,821, 13,271, 8775, 18,778, and 10,109 images, respectively, and their respective resolutions are 640 \(\times\) 480, 3840 \(\times\) 2160, 752 \(\times\) 480, 1920 \(\times\) 1080 pixels, and various resolutions. Their samples are shown in Figure 1. Compared to other UAV datasets, the Real World dataset stands out for its diversity in terms of UAV types and environments, despite the low-resolution quality of the images. This is because all the data in the Real World dataset are obtained from YouTube videos, whereas other datasets are typically collected by the researchers themselves. On the other hand, the Det-Fly dataset addresses the shortcomings of single-view UAV data by providing multi-view data that covers a wider range of angles and perspectives. The MIDGARD dataset and USC-Drone dataset also contain data from a single type of UAV and a richer environment, but they are limited by the disadvantage of having a single viewpoint. The DUT-Anti-UAV contains more than 35 types of UAVs with various background. The background in the dataset includes a wide variety of environments, such as the sky, black clouds, jungle, high-rise building, residential building, farmland, and playground. In addition, various lighting conditions (including dawn, day, dusk and night) as well as different weather conditions are taken into account. The datasets referenced, namely the Real World dataset, the Det-Fly dataset, the MIDGARD dataset, the USC-Drone dataset, and the DUT-Anti-UAV, can be accessed through the following provided links: [[https://github.com/[PERSON]/DroneDetectionDataset](https://github.com/Maciullo/DroneDetectionDataset)]([https://github.com/Maciullo/DroneDetectionDataset](https://github.com/Maciullo/DroneDetectionDataset)), [[https://github.com/Jake-WU/Det-Fly](https://github.com/Jake-WU/Det-Fly)]([https://github.com/Jake-WU/Det-Fly](https://github.com/Jake-WU/Det-Fly)), [[https://mrs.felk.cvut.cz/midgard](https://mrs.felk.cvut.cz/midgard)]([https://mrs.felk.cvut.cz/midgard](https://mrs.felk.cvut.cz/midgard)), [[https://github.com/chelicynly/A-Deep-Learning-Approach-to-Drone-Monitoring](https://github.com/chelicynly/A-Deep-Learning-Approach-to-Drone-Monitoring)]([https://github.com/chelicynly/A-Deep-Learning-Approach-to-Drone-Monitoring](https://github.com/chelicynly/A-Deep-Learning-Approach-to-Drone-Monitoring)) and [[https://github.com/wangdongdut/DUT-Anti-UAV](https://github.com/wangdongdut/DUT-Anti-UAV)]([https://github.com/wangdongdut/DUT-Anti-UAV](https://github.com/wangdongdut/DUT-Anti-UAV)), accessed on 1 June 2023.  
There are several publicly available video-based UAV datasets datasets, the Anti-UAV [79] and the USC-GRAD-STDdb [80]. The DUT-Anti-UAV also contain 24,804 videos, and their resolutions are mostly 1920 \(\times\) 1080 pixels. There are 318 RGB-T pairs in the Anti-UAV dataset, each of which consists of an RGB video and a thermal video. A 25 FPS  
Figure 1: Some example images in (**a**) Real World, (**b**) Det-Fly, (**c**) MIDGARD, and (**d**) USC-Drone.  
frame rate was used to record these videos, which feature various UAVs flying in the sky. It is publicly available at [[https://github.com/ucas-vg/Anti-UAV](https://github.com/ucas-vg/Anti-UAV)]([https://github.com/ucas-vg/Anti-UAV](https://github.com/ucas-vg/Anti-UAV)), accessed on 1 June 2023. Meanwhile, in USC-GRAD-STDdb dataset, there are 115 video segments, with a total of over 25,000 annotated frames in HD 720p resolution. The USC-GRAD-STDdb dataset is available at [[https://citius.usc.es/t/usc-grad-stddb](https://citius.usc.es/t/usc-grad-stddb)]([https://citius.usc.es/t/usc-grad-stddb](https://citius.usc.es/t/usc-grad-stddb)), accessed on 1 June 2023.