# Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
## 3 Related Work
### Drone Detection  
In recent years, the application of deep learning based detection methods led to excellent results for a wide range of applications including drone detection. Due to the absence of large amounts of drone detection datasets, a two-staged detection strategy has been proposed in [25]. The authors examined the suitability of different flying object detection techniques, i.e., frame differencing and background subtraction techniques, locally adaptive change detection and object proposal techniques [26], to extract region candidates in video data from static and moving cameras. In the second stage, a small CNN classification network is applied to distinguish each candidate region into the categories drone and clutter. In [27], a detection framework able to cope with targets of different size (in the range of less than ten to hundreds of pixels) as well as with varying illumination conditions has been proposed. Similar to [25], the approach consists of a first module used to detect the most probable regions containing the target, followed by a second module that classifies the detected target in either drone or other flying entity (e.g., a bird). The first module adopts the Region Proposal Network [28]--a deep learning based object proposal technique--to extract candidate regions, which are classified in the subsequent module by applying a CNN classifier. Spatio-temporal semantic segmentation has been exploited in [29] to detect small targets based on two separate CNNs. In a first step, a U-Net architecture is used to identify the areas of interest in an up-scaled version of the input image [30]. Then, the classification step is performed by resorting to a ResNet network, which selects the most probable areas containing the target of interest. The entire process is further enhanced by using spatio-temporal patches that represent typical drones trajectories; in doing so, the detector can more easily distinguish drones from birds. The adoption of different CNN architectures (e.g., Zeiler-Fergus (ZF) [31], Visual Geometry Group (VGG16) [32]) for drone detection has been investigated in [33]. To overcome the limited amount of data available for training the deep networks, authors exploited transfer learning from ImageNet and performed a pre-training to fine-tune the models. The experimental results revealed that VGG16 with Faster Region Based Convolutional Neural Network (R-CNN) achieves the best performance among all the considered architectures. The authors of Drone Detection in Long-Range Surveillance [34] worked on a previous iteration of the same dataset, with quite good results in the detection of small objects. They applied a Faster R-CNN network with various backbones and showed that ResNet-101 had the best results. In [35], an end-to-end detection algorithm able to predict the presence of a target in a sequence of video frames is presented. It is based on the adoption of the deep learning based detection method termed YOLOv2 [36], whose training is performed using an artificial dataset obtained by mixing real birds and drones images, each with a different background. The obtained results demonstrate that the diversity and the scale of the dataset have a positive impact on the detection and tracking processes. In [37], the authors adopted YOLOv2 for drone detection in images of a multi-camera array, while [PERSON] et al. [38] proposed the usage of its more recent variant YOLOv3 [39] for drone detection in a tracking pipeline. The applicability of the Single Shot MultiBox Detector (SSD) in an image processing pipeline for long range drone detection has been demonstrated in [12]. In [40], Super-Resolution (SR) techniques have been incorporated in the detection pipeline, with the aim of increasing the maximum detection range of the system. Before applying the detection algorithm, the input image is up-scaled by a factor of 2 by means of a deep DCSCN model [41].