![]() To achieve effective detection of drones, in building the classes, we have considered including other flying objects that are likely to be mistaken for a drone . Three different consumer-grade drones are included in the dataset together with birds, airplanes and helicopters, which constitutes the published dataset with the biggest number of target classes (drone, bird, airplane and helicopter, in comparison to others which only contain three, two or one of these classes only). Another fundamental challenge is the lack of public reference databases that serve as a benchmark for researchers . Our work also encompasses collecting and annotating a public dataset to accomplish training and evaluation of the system. Despite this, research in sensor fusion for drone detection is scarce . In addition to effective detection, classification and tracking methods, the existing literature also points out sensor fusion as a critical open area to achieve more accuracy and robustness compared to a single sensor. Our developments are built on state-of-the-art machine learning techniques, extending methods from conclusions and the related literature recommendations . In this work, we address the design and evaluation of an automatic multi-sensor drone detection and tracking system. This rapid development in the use and potential misuse of UAVs has consequently produced an increase in research on drone detection to counteract potential risks due to intrusion in restricted areas, either intentional or unintentional. Several near-misses and verified collisions with UAVs have involved hobbyist drone operators violating aviation safety regulations, sometimes without knowledge. An increasingly common risk is the report of drone sightings in restricted airport areas, which ultimately has led to total closure of the airport and cancellation of hundreds of flights . For example, an aircraft can be severely damaged if it collides with a consumer-sized drone, even at moderate speeds , and an ingested drone can rapidly disable an aircraft engine. Unfortunately, they can also be intentionally or unintentionally misused, threatening the safety of others. Their low cost and ease of operation have caused drones to find their way into consumer use just for recreation and entertainment as well . ![]() Sensor fusion is also explored, showing that the system can be made more robust in this way, mitigating false detections of the individual sensors.ĭrones, also referred to small and remotely controlled unmanned aerial vehicles (UAVs), can fulfil valuable societal roles such as law enforcement, medical, construction, search and rescue, parcel delivery, remove area exploration, topographic mapping, forest/water management, or inspection of big infrastructures like power grids. Two other novelties of our work are the creation of a new public dataset of multi-sensor annotated data that expands the number of classes compared to existing ones, as well as the study of the detector performance as a function of the sensor-to-target distance. The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution. ![]() However, our final deployment has not included the latter due to its limited detection range. ![]() The sensing solutions are complemented with an ADS-B receiver, a GPS receiver, and a radar module. Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest. In conjunction with standard video cameras and microphone sensors, we explore the use of thermal infrared cameras, pointed out as a feasible and promising solution that is scarcely addressed in the related literature. Here, we design and evaluate a multi-sensor drone detection system. Automatic detection of flying drones is a key issue where its presence, especially if unauthorized, can create risky situations or compromise security.
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