# Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
## 2 Drone vs. Bird Detection Challenge 2020
### Dataset  
The training set for the challenge consists of 77 different videos. The videos comprise 1384 frames on average, while each frame contains on average 1.12 annotated drones. Eleven of these videos originate from the previous installment of the challenge and were collected using MPEG4-coded static cameras by the SafeShore project and by the Fraunhofer IOSB research institute. In 2020, 45 further videos have been contributed by the ALADDIN project in 3 different resolutions, namely 1920 \(\times\) 1080 @25 fps, 720 \(\times\) 576 @50 fps, 1280 \(\times\) 720 @30 fps. The videos have been recorded at two locations with different geo-characteristics. The videos contain 7 types of drones: 3 with fixed wings and 4 rotary ones. The sequences are mostly short in duration (about 1 min each) with the exception of 2 videos that are 5 min long. The remaining 21 training videos were provided by Fraunhofer IOSB and were recorded at different locations in Germany. Such videos include 5 rotary drone types. In total, the videos exhibit 8 different types of drones (see Figure 1).  
Overall, the training set exhibits a high variability in difficulty, including sequences with sky or vegetation as background, different weather types (cloudy, sunny), direct sun glare and variation in camera characteristics (see Figure 2). Moreover, the distance of the drones from the camera varies strongly across and within the videos, yielding large variations in drone sizes as shown in Figure 3. The drone sizes are in the range between 15 pixels and more than 1,000,000 pixels. The majority of annotated drones exhibits a size less than \(16^{2}\) pixels or in the range between \(16^{2}\) and \(32^{2}\) pixels. The small size of occurring drones makes the detection task very challenging. Many of the videos are recorded with a static camera but also moving camera recordings are included. Each video is associated with a separated annotation file containing the frame number and the bounding box (expressed as \([\text{top}_{x}\text{ top}_{y}\) width height]) for the frames in which drones enter the scenes. Birds not individually annotated occur in several of the videos and are the main disturbing object type (see Figure 4).  
Figure 1: Examples of drone types present in the dataset, i.e., Parrot Disco, 2 custom fix wing drones, DJI Inspire, DJI Phantom, DJI Mavic, DJI Matrice and 3 DR Solo Robotics.  
Figure 2: Sample frames extracted from the training videos exhibit the large variability of the dataset.  
The challenge test set consists of further 14 videos for which no annotations are provided. The videos contain similar characteristics to the training set. Some of the locations displayed in the test set are also contained in the training set, but not all. The test set contains three sequences with moving camera. Longer sequences were shortened to 1 minute in order to avoid individual videos from dominating the resulting evaluation scores. The dataset is freely available for download upon signing a Data Usage Agreement (DUA). The annotations are available at [[https://github.com/wosdetc/challenge](https://github.com/wosdetc/challenge)]([https://github.com/wosdetc/challenge](https://github.com/wosdetc/challenge)), accessed on: 14 April 2021.