Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work presents three different original approaches that competed in a grand challenge on the “Drone vs. Bird” detection problem. The goal is to detect one or more drones appearing at some time point in video sequences where birds and other distractor objects may be also present, together with motion in background or foreground. Algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. In particular, three original approaches based on different deep learning strategies are proposed and compared on a real-world dataset provided by a consortium of universities and research centers, under the 2020 edition of the Drone vs. Bird Detection Challenge. Results show that there is a range in difficulty among different test sequences, depending on the size and the shape visibility of the drone in the sequence, while sequences recorded by a moving camera and very distant drones are the most challenging ones. The performance comparison reveals that the different approaches perform somewhat complementary, in terms of correct detection rate, false alarm rate, and average precision.

This paper presents the 6th edition of the Drone-vs-Bird detection challenge, jointly organized with the WOSDETC workshop within the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023. The main objective of the challenge is to advance the current state-of-the-art in detecting the presence of one or more Unmanned Aerial Vehicles (UAVs) in real video scenes, while facing challenging conditions such as moving cameras, disturbing environmental factors, and the presence of birds flying in the foreground. For this purpose, a video dataset was provided for training the proposed solutions, and a separate test dataset was released a few days before the challenge deadline to assess their performance. The dataset has continually expanded over consecutive installments of the Drone-vs-Bird challenge and remains openly available to the research community, for non-commercial purposes. The challenge attracted novel signal processing solutions, mainly based on deep learning algorithms. The paper illustrates the results achieved by the teams that successfully participated in the 2023 challenge, offering a concise overview of the state-of-the-art in the field of drone detection using video signal processing. Additionally, the paper provides valuable insights into potential directions for future research, building upon the main pros and limitations of the solutions presented by the participating teams.

Vehicle detection in aerial images is a crucial image processing step for many applications like screening of large areas as used for surveillance, reconnaissance or rescue tasks. In recent years, several deep learning based frameworks have been proposed for object detection. However, these detectors were developed for datasets that considerably differ from aerial images. In this article, we systematically investigate the potential of Fast R-CNN and Faster R-CNN for aerial images, which achieve top performing results on common detection benchmark datasets. Therefore, the applicability of eight state-of-the-art object proposal methods used to generate a set of candidate regions and of both detectors is examined. Relevant adaptations to account for the characteristics of the aerial images are provided. To overcome the shortcomings of the original approach in case of handling small instances, we further propose our own networks that clearly outperform state-of-the-art methods for vehicle detection in aerial images. Furthermore, we analyze the impact of the different adaptations with respect to various ground sampling distances to provide a guideline for detecting small objects in aerial images. All experiments are performed on two publicly available datasets to account for differing characteristics such as varying object sizes, number of objects per image and varying backgrounds.

Automated detection of objects in aerial imagery is the basis for many applications, such as search and rescue operations, activity monitoring or mapping. However, in many cases it is beneficial to employ a detector on-board of the aerial platform in order to avoid latencies, make basic decisions within the platform and save transmission bandwidth. In this work, we address the task of designing such an on-board aerial object detector, which meets certain requirements in accuracy, inference speed and power consumption. For this, we first outline a generally applicable design process for such on-board methods and then follow this process to develop our own set of models for the task. Specifically, we first optimize a baseline model with regards to accuracy while not increasing runtime. We then propose a fast detection head to significantly improve runtime at little cost in accuracy. Finally, we discuss several aspects to consider during deployment and in the runtime environment. Our resulting four models that operate at 15, 30, 60 and 90 FPS on an embedded Jetson AGX device are published for future benchmarking and comparison by the community.

Drones are being used more and more frequently and in various ways to attack people and critical infrastructures, which increases their level of danger and makes early detection and risk assessment even more difficult. There is no ultimate drone sensor. Rather, drones can only be effectively detected, if suitable sensor technologies are integrated within a system. Among the best suited sensorics for this purpose are radar and optical sensors, because the advantage of the radar, given by the long detection range, can be well combined with the additional visual-based information gained from a camera in the mid- and short-range, where usually the radar fails detecting micro drones. In this paper, a modular drone detection and assistance system MODEAS is presented, which can be used to automatically detect and track drones within a range up to some kilometers on the sky, but also in complex urban environments, where buildings and vegetation offer a perfect background for camouflage. The system provides an early detection of flying objects as potential drones, when those are not at all or only as small dots visible for the human eye. Main strengths of the system are the robust AI detection to differentiate such an object against clutter (birds, clouds) and its further classification by model and payload recognition, the latter aspect being considered as an indication of dangerousness. Moreover, once a drone is detected, the system follows it automatically, precisely and smoothly with a high-resolution pan-tilt-camera, measuring the distance to the sensor station with a laser rangefinder, compute the geocoordinates and visualize them live on a map. To improve the precision of the detection, a multisensorial fusion of all sensor detections is performed. Furthermore, alarms may be generated when a drone enters a restricted area.