# Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
## 3 Related Work  
### Drone Datasets  
In the following, an overview of existing drone detection datasets that are publicly available is provided. Note that only datasets are reported that comprise EO or IR imagery, while drone detection datasets based on other sensor modalities are not considered. The Drone Dataset: Amateur Unmanned Air Vehicle Detection [18] published in 2019 comprises more than 4000 images with drones, whereof the most images contain a DJI  
Figure 5: Research groups that participated to the Drone vs. Bird Challenge up to March 2021. Phantom. The image resolution varies between 300 \(\times\) 168 pixels and 4k. In addition, the dataset contains images with non-drone objects. The Small Target Detection database (USC-GRAD-STDdb) [19] comprises 115 video segments retrieved from YouTube. The image resolution is 1280 \(\times\) 720 pixels and annotations are provided for more than 25,000 frames. In total, more than 56,000 small objects whose sizes are in the range between 4 \(\times\) 4 and 16 \(\times\) 16 pixels are annotated. The small objects are categorized into drone, bird, boat, vehicle and person. 57 video segments with 12,139 frames contain either drones or birds. The Purdue UAV dataset [20] consists of 5 video sequences of 1829 frames. The video sequences are recorded by a camera mounted on a custom airframe with 30 frames per second. The image resolution is either 1920 \(\times\) 1080 or 1280 \(\times\) 960 pixels. Each video sequence comprises up to 4 drones. Ground truth annotations for each video sequence are publicly available. The Flying Object Detection from a Single Moving Camera dataset [21] comprises 20 video sequences with UAVs. Each image has a resolution of 752 \(\times\) 480 pixels and contains up to two objects of the same model. The video sequences are recorded by a camera mounted on a flying drone. The variable appearance of drones due to changing attitudes, lighting conditions, and even aliasing and saturation caused by small sizes makes the dataset challenging. In addition, 20 video sequences with aircraft retrieved from Youtube are provided. The image resolution of these video sequences varies between 640 \(\times\) 480 to 1280 \(\times\) 720 pixels. Recently, the Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection [22] has been published. The dataset consists 51446 images for training and 5375 images for testing. The images are either downloaded from the Internet or recorded by the authors. As the original image resolution is in the range between 640 \(\times\) 480 pixels and 4 k, all images are scaled to 640 \(\times\) 480 pixels. The train set comprises 52,676 drone instances, while the test set only contains 2863 drone instances, so that 2750 images of the test set do not contain any drone. A semi-automated labelling pipeline has been used to speed-up the annotation task. In the train set, about 40.8% of drones are smaller than 32\({}^{2}\) pixels, while 23.4% are larger than 96\({}^{2}\) pixels. In the test set, approximately 36.3% of drones are smaller than 32\({}^{2}\) pixels, while 28.3% are larger than 96\({}^{2}\) pixels. The Anti-UAV Challenge dataset [23] has been published in 2020. The 1 st Anti-UAV Challenge was part of the of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020. In contrast to the Drone vs. Bird Detection Challenge, the task of the Anti-UAV Challenge is single object tracking. The dataset comprises 160 video sequences (both IR and EO). The resolution of the IR images is 640 \(\times\) 512 pixels and the resolution of the EO images is 1920 \(\times\) 1080 pixels. Annotations are provided for 100 video sequences to train the tracking algorithms, while only the bounding boxes are given for the first frame of each test video. The videos are recorded by a static ground camera with an automatic rotation platform that can be remotely controlled by a computer. Hence, the image content is limited to only a few scenarios.