The best-tracked target is defined as the one with the longest track history. The dataset can be used to develop new algorithms for drone detection using multi-sensor fusion from infrared and visible videos and audio files. This table also contains information about the exact drone type and if the clip comes from the Internet or not. Some other parts from Actobotics are also used in the mounting of the system, and the following has been designed and 3D-printed: adapters for the IR-, video- and fish-eye lens cameras, and a case for the servo controller and power relay boards. The Some instructions and examples are found in "Create_a_dataset_from_videos_and_labels.m", Please cite: Taha B., Shoufan A. The output from the IRcam is sent to the laptop via a USB-C port at a rate of 60 frames per second (FPS). To allow studies as a function of the sensor-to-target distance, the dataset is divided into three categories (Close, Medium, Distant) according to the industry-standard Detect, Recognize and Identify (DRI) requirements [7], built on the Johnson criteria [8]. Video labels: Airplane, Bird, Drone and Helicopter. The captured data is from a thermal infrared camera (IRcam), a camera in the visible range (Vcam), and a microphone. Results of this challenge will be presented and discussed in a special session of ICMCIS. They are placed together on a pan/tilt platform that can be aimed in specific directions. This is because it has not been possible to film all types of suitable targets, given that this work has been carried out during the drastic reduction of flight operations due to the COVID19 pandemic. To feed the laptop via USB, we use an Elgato Cam Link 4K frame grabber, which gives a 1280720 video stream in YUY2- format (16 bits per pixel) at 50 FPS. The most critical points applicable to the drones and locations used in this work are: Since the drones must be flown within visual range, the dataset is recorded in daylight, even if the thermal and acoustic sensors could be used even in complete darkness. The DC-DC solution is used when the system is deployed outdoors and, for simplicity, it uses the same battery type as one of the available drones. Most of the existing studies on drone detection fail to specify the type of acquisition device, the drone type, the detection range, or the employed dataset. government site. Learn more Link to Data in Brief. Institution: School of Information Technology, Halmstad University. The USC drone detection and tracking dataset with user labeled bounding boxes is available to the public. If nothing is detected by the Fcam, the platform can be set to move in two different search patterns to scan the sky around the system. Dag WILHELMSEN, Norway, MS Cristina MELILLA, University of Udine, Dept. This years challenge is to identify and track unmanned airborne systems (UAS) or drones. An example from a labeling session is shown in Fig. Importing Matlab files into a Python environment can also be done using the scipy.io.loadmat command.
Whitepaper on thermal DRI. The IRcam is also powered via a USB connection. The dataset contains 90 audio clips and 650 videos (365 IR and 285 visible). The data does not include human subjects or animals. Since the drones must be flown within visual range, the largest sensor-to-target distance for a drone is 200m. There are also eight clips (five IR and three visible videos) within the dataset with two drones flying simultaneously, as shown, for example, in Fig. Beside, we have not found any previous study that addresses the detection task as a function of distance to the target. Andrasi P. Night-time detection of UAVs using thermal infrared camera. Author F. A.-F. thanks the Swedish Research Council and VINNOVA for funding his research. of Mathematics, Computer Science and Physics, DR Andrea TOMA, University of Udine,
Peter LENK, NATO Communications and Information Agency, Vice-Chairs: It also includes other flying objects that can be mistakenly detected as drones, such as birds, airplanes or helicopters.
If the detection system is to be placed, for example, on-board a drone, it must also be considered that it would affect battery duration, reducing the effective flying time of the drone. IR_DRONE_001.mp4. Samaras S., Diamantidou E., Ataloglou D., Sakellariou N., Vafeiadis A., Magoulianitis V., Lalas A., Dimou A., Zarpalas D., Votis K., Daras P., Tzovaras D. Deep learning on multi sensor data for counter UAV applications a systematic review. 7 shows the main parts of the system. Given the resolution and field of view of the IRcam and the object class sizes: Drone 0.4m, bird 0.8m, helicopter110m and airplane220m, we get a distance division for the different object types summarized in Table4. There are 30 files of each of the three output audio classes indicated in Table1. its variants. F. Svanstrm, C. Englund, F. Alonso-Fernandez, Real-Time Drone Detection and Tracking with Visible, Thermal and Acoustic Sensors. However, this detection was done manually by a person looking at the live video stream. of Mathematics, Computer Science and Physics, Chair: This outputs a 1024768 video stream in Mjpg-format at 30 FPS via a USB connector. To achieve the pan/tilt motion, two Hitec HS-7955TG servos are used. The drone detection system used in this project utilized several sensors at the same time, including sensor fusion. The three drone types of the dataset. It might be possible to use a simple microcontroller if the drone detection system trained and evaluated with the dataset uses only one sensor or a small number of them. Link to thesis Drone detection, UAV detection, Anti-drone systems. YouTube channel VIRTUAL AIRFIELD operated by SK678387. A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work. The military scenario to this challenge is to improve capabilities to protect people and equipment against the threat of misuse of small (Class I) UAS such as hobby drones. Due to the limited field of view of these cameras, they are steered towards specific directions guided by a fish-eye lens camera (Fcam) covering 180 horizontally and 90 vertically. Shi X., Yang C., Xie W., Liang C., Shi Z., Chen J. Anti-drone system with multiple surveillance technologies. "Svanstrm F. (2020). Dataset containing IR, visible and audio data that can be used to train and evaluate drone detection sensors and systems. The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article. All computations and acquisitions are made on a Dell Latitude 5401 laptop, having an Intel i7-9850H CPU and an Nvidia MX150 GPU. The Medium bin stretches from where the target is from 15 down to 5 pixels, hence around the DRI detection point, and the Distant bin is beyond that. The holder for the servo controller and power relay boards is placed behind the pan servo inside the aluminium mounting channel. Fig. sharing sensitive information, make sure youre on a federal To supply the servos with the necessary voltage and power, both a net adapter and a DC-DC converter are available. IR_DRONE_001_LABELS.mat. 2017. Drone Detection and Classification using Machine Learning and Sensor Fusion". Occasionally occurring in the dataset are also the black kite (Milvus migrans) of the Accipitridae family and the Eurasian skylark (Alauda arvensis) of the lark family (Alaudidae). Free to download, use and edit. (a) Hubsan H107D+. The experiments show that, even being trained on synthetic data, the proposed system performs well on real world drone images with complex background. Example of IR video with two drones appearing in the image.
To illustrate the detect, recognize, and identify concept, objects from all the target classes being 15 pixels in width are shown in Fig. Loren DIEDRICHSEN, USA (c) DJI Flame Wheel F450. 5. (a) An airplane at a distance of 1000m. (b) A bird at a distance of 40m. (c) A drone at at distance of 20m. (d) A helicopter at a distance of 500m. To compose the dataset, three different drones are used. The intended purpose of the dataset is to be used in the training and evaluation of stationary drone detection systems on the ground. If the dataset is to be used in another development environment, the label files can be opened in Matlab, and the content is saved in the desired format, such as .csv. When flown, the unmanned aircraft shall be within its operational range and well within the pilot's visual line of sight. The filenames start with the sensor type, followed by the target type and a number, e.g. The distribution of the 285 visible videos. The .gov means its official. It contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 identities for person re-identification, and 22,263 frames for attribute recognition. Cristofer Englund: Conceptualization, Supervision, Writing review & editing. In addition to using several different sensors, the number of classes is higher than in previous studies [4]. Proceedings of the International Conference on Computer Vision Systems. Careers. This work has been carried out by Fredrik Svanstrm in the context of his Master Thesis at Halmstad University (Master's Programme in Embedded and Intelligent Systems). This is followed by a multi-object Kalman filter tracker, which, after calculating the position of the best-tracked target, sends the azimuth and elevation angles. Fig. or UAV-Human is a large dataset for human behavior understanding with UAVs. Datasets covering a range of scenarios, sensor types and drones are available on Kaggle for theICMCIS drone detection challenge. The database is complemented with 90 audio files of the classes drones, helicopters and background noise. The benchmarks section lists all benchmarks using a given dataset or any of It contains infrared and visible videos and audio files of drones, birds, airplanes, helicopters, and background sounds. In that paper, the authors were able to detect three different drone types up to 100m. When flying within airports' control zones or traffic information zones and if you do not fly closer than 5km from any section of the airport's runway(s), you may fly without clearance if you stay below 50m from the ground. In some few cases, these vehicles fly at very low speed or are hovering. THE UNIVERSITY OF SOUTHERN CALIFORNIA SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE OR NON-INFRINGEMENT. Accessibility All computations are made on a standard laptop. Notably, the Boson sensor of the FLIR Breach has a higher resolution than the one used in [11] where a FLIR Lepton sensor with 8060 pixels was used. Infiniteoptics. The annotation of the respective clips has the additional tag LABELS, e.g. For the protection of people, animals and property which are unrelated to the flight, there must be a horizontal safety distance between these and the unmanned aircraft throughout the flight. 2. will also be available for a limited time. Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks.APSIPA Transactions on Signal and Information Processing8 (2019). slightly different versions of the same dataset. A Pololu Mini Maestro 12-Channel USB servo controller is included so that the respective position of the servos can be controlled from the laptop. The largest distance between the sensors and a drone in the database is 200m. All videos are in mp4 format.
Bethesda, MD 20894, Web Policies We use variants to distinguish between results evaluated on On the pan and tilt platform in the middle are the IR- and video cameras. Descriptions of the videos are found in "Video_dataset_description.xlsx". Link to ICPR2020-paper 6. The new PMC design is here! This is the Servocity DDT-560H direct drive tilt platform together with the DDP-125 Pan assembly, also from Servocity. Since the distance bin information of the clip is not included in the filename, there is also an associated excel-sheet where this is shown in a table. The dataset was collected by a flying UAV in multiple urban and rural districts in both daytime and nighttime over three months, hence covering extensive diversities w.r.t subjects, backgrounds, illuminations, weathers, occlusions, camera motions, and UAV flying attitudes. The dataset can be used for multi-sensor drone detection and tracking. The https:// ensures that you are connecting to the Fernando Alonso-Fernandez: Conceptualization, Supervision, Funding acquisition, Writing original draft. 3. The color palette can be changed for the interpolated image format, and several other image processing features are also available. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). The drone flights are all done in compliance with the Swedish national rules for unmanned aircraft found in [10]. 2018. Chair: Examples of varying weather conditions in the dataset. Both the videos and the audio files are cut into ten-second clips to be easier to annotate. Each clip is of ten seconds, resulting in a total of 203,328 annotated frames. The version used in this work is an F450 quadcopter. The F450 drone flying at a distance of 3m in front on the IRcam. 
Aviation International News . These files are Matlab Ground-Truth objects and using the Matlab video labeller app, the videos and respective label files can easily be opened, inspected, and even edited. All participants in this data challenge are invited to take part in the special session. aAir Defence Regiment, Swedish Armed Forces, Sweden, bCenter for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad SE 301 18, Sweden, cRISE, Lindholmspiren 3A, Gothenburg SE 417 56, Sweden. To record data in the visible range of the spectrum, a Sony HDR-CX405 video camera (Vcam) is used, which provides data through an HDMI port. These drones differ in size, with Hubsan H107D+ being the smallest, with a side length from motor-to-motor of 0.1m. The Phantom 4 Pro and the DJI Flame Wheel F450 are slightly larger with 0.3 and 0.4m motor-to-motor side lengths, respectively. The provided data can help in developing systems that distinguish drones from other objects that can be mistaken for a drone, such as birds, airplanes or helicopters. A Pololu Mini Maestro 12-Channel USB servo controller is included. This goes in parallel with (intentional or unintentional) misuse episodes, with an evident threat to the safety of people or facilities [1]. THE USC DRONE DATASET PROVIDED HEREUNDER IS ON AN AS IS BASIS, AND THE UNIVERSITY OF SOUTHERN CALIFORNIA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. As a result, the detection of UAV has also emerged as a research topic [2]. The conference will also feature a keynote presentation on the challenges of UAS by Liisa Janssens, co-author ofA Comprehensive Approach to Countering Unmanned Aircraft Systems. Three different drones are used to collect and compose the dataset: Hubsan H107D+, a small-sized first-person-view (FPV) drone, the high-performance DJI Phantom 4 Pro, and finally, the medium-sized kit drone DJI Flame Wheel in quadcopter (F450) configuration. It should be possible, however, to use the database (or parts of it) on-board a drone if, for example, the purpose of such drone is to find other drones. An official website of the United States government. Given that the drones must be flown within visual range due to regulations, the largest sensor-to-target distance for a drone in the dataset is 200m, and acquisitions are made in daylight. Dataset containing IR, visible and audio data to be used to train drone detection systems. Papers With Code is a free resource with all data licensed under, UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles. The role of the fish-eye camera is not to detect specific classes but to detect moving objects in its field of view. explicitly telling what kind of helicopter it is and so on. The captured videos are recorded at locations in and around Halmstad and Falkenberg (Sweden), at Halmstad Airport (IATA code: HAD/ICAO code: ESMT), Gothenburg City Airport (GSE/ESGP) and Malm Airport (MMX/ESMS). IN NO EVENT SHALL THE UNIVERSITY OF SOUTHERN CALIFORNIA BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THE USC DRONE DATASET, EVEN IF THE UNIVERSITY OF SOUTHERN CALIFORNIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. The audio in the dataset is taken from the videos or recorded separately. The IR videos have a resolution of 320256 pixels, whereas the visible videos have 640512. The distribution of videos among the four output video classes is shown in Tables2 and and3.3. The background sound class contains general background sounds recorded outdoor in the acquisition location and includes some clips of the sounds from the servos moving the pan/tilt platform where the sensors were mounted. Guvenc I., Koohifar F., Singh S., Sichitiu M.L., Matolak D. Detection, tracking, and interdiction for amateur drones. Jrgen GROSCHE, Germany The latter can be built both as a quadcopter (F450) or in a hexacopter configuration (F550). The Close distance bin is from 0m out to a distance where the target is 15 pixels wide in the IRcam image, i.e. Audio labels: Drone, Helicopter and Background. The field of view of the IR-camera is 24 horizontally and 19 vertically. Chevalier P. ResearchGate publication; 2016. Permission is hereby granted, free of charge, to any person obtaining a copy of the database and associated documentation files (the USC DRONE DATASET), to deal in the database without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, and/or sell copies of the USC DRONE DATASET, and to permit persons to whom the dataset is furnished to do so, provided that the above copyright notice(s) and this paragraph and the following two paragraphs appear in all copies of the USC DRONE DATASET and in supporting documentation. Due to its adjustable zoom lens, the field of view of the Vcam can be set to different values, which in this work is set to about the same field of view as the IRcam. The biggest challenge in adopting deep learning methods for drone detection is the limited amount of training drone images. The distance bin division for the different target classes. If you use this dataset in your work, please cite related papers: Wang, Ye, Yueru Chen, Jongmoo Choi, and C-C. Jay Kuo.
A multi-object Kalman filter tracker then steers the infrared and visible cameras via a servo controller mounted on a pan/tilt platform. Based on this, the pan/tilt platform servos are then steered via the servo controller so that the moving object can be captured by the infrared and visible cameras. 8 shows an image taken from the IRcam video stream. 1. Machine learning-based drone detection and classification: state-of-the-art in research. On the lower left is the microphone, and above that is the fish-eye lens camera. (b) DJI Phantom 4 Pro. The audio part has 90 ten-second files in wav-format with a sampling frequency of 44100Hz. Since one of the objectives of this work is to explore performance as a function of the sensor-to-target distance, the video dataset has been divided into three distance category bins: Close, Medium and Distant. PMC legacy view Received 2021 Mar 12; Revised 2021 Sep 13; Accepted 2021 Oct 21. The videos can be used as they are, or together with the respective label-files. The use of small and remotely controlled unmanned aerial vehicles (UAVs), referred to as drones, has increased dramatically in recent years, both for professional and recreative purposes. The site is secure. What happens when a drone hits an airplane wing? "Svanstrm F, Alonso-Fernandez F and Englund C. (2021). The acquisition sensors are mounted on a pan-tilt platform that steers the cameras to the objects of interest. and transmitted securely. The sensors employed are specified in the next section. To have a stable base, all hardware components, except the laptop, are mounted on a standard surveyor's tripod. official website and that any information you provide is encrypted The annotation of the video dataset is done using the Matlab video labeller app. http://dx.doi.org/10.1109/ICPR48806.2021.9413241, https://www.infinitioptics.com/sites/default/files/attachments/Infiniti%20DRI%20Whitepaper.pdf, https://www.youtube.com/channel/UCx-PY5Q1Z5sJOQ9e8wvwvWQ. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. The sound of the distinct classes of the database is captured with a Boya BY-MM1 mini cardioid directional microphone, which is also connected to the laptop. The laptop is connected to all the sensors mentioned above and the servo controller using the built-in ports and an additional USB hub. 1Bell 429, one of the helicopter types in the dataset, has a length of 12.7m. 2Saab 340 has a length of 19.7m and a wingspan of 21.4m. National Library of Medicine The majority of the solutions developed to counter such UASssofar use a mix of sensors to detect and track drones entering a protected flight zone. FOIA of Mathematics, Computer Science and Physics, International Conference on Military Communications and Information Systems, A Comprehensive Approach to Countering Unmanned Aircraft Systems, Camera ready paper upload deadline: 10.06.2022. the requirement for recognition according to DRI. All three types can be seen in Fig.
All sensors and the platform are controlled with a standard laptop vis a USB hub. This also facilitates transport and deployment outdoors, as shown in the right part of the figure. The Fcam is used to feed a foreground/background detector based on Gaussian Mixture Models (GMM), which produces binary masks of moving objects. (b) The system deployed just north of the runway at Halmstad airport (IATA/ICAO code: HAD/ESMT). The drones and helicopters appearing in the database move in most cases at normal flying speeds (in the range of 060km/h for drones, and 0300km/h for helicopters). These are of the following types: Hubsan H107D+, a small first-person-view (FPV) drone; the high-performance DJI Phantom 4 Pro; and the medium-sized DJI Flame Wheel. Fredrik Svanstrm: Conceptualization, Methodology, Investigation, Data curation, Writing review & editing. Therefore, the computational cost is relatively high, and hence a laptop with a separate GPU was used. Federal government websites often end in .gov or .mil. The video part contains 650 infrared and visible videos (365 IR and 285 visible) of drones, birds, airplanes and helicopters.
You signed in with another tab or window. This dataset can be used for UAV-based human behavior understanding, including action recognition, pose estimation, re-identification, and attribute recognition. (a) The main parts of the system.
Some tasks are inferred based on the benchmarks list. Pan/tilt motion is achieved with two Hitec HS-7955TG servos. To address this issue, we develop a model-based drone augmentation technique that automatically generates drone images with a bounding box label on drones location. The lack of proper UAV detection studies employing thermal infrared cameras is also acknowledged as an issue, despite its success in detecting other types of targets [2]. Typical sensors are radar or radio direction finding, data from both types of sensor are included in the dataset. Dept. When flown in uncontrolled airspace, the drone must stay below 120m from the ground. and ImageNet 6464 are variants of the ImageNet dataset. To get a more comprehensive dataset, both in terms of aircraft types and sensor-to-target distances, our data has been completed with non-copyrighted material from the YouTube channel ``Virtual Airfield operated by SK678387'' [9], in particular 11 plus 38 video clips in the airplane and helicopter categories, respectively. The clips are annotated with the filenames themselves, e.g. If all images are extracted from all the videos the dataset has a total of 203328 annotated images. Before This dataset can be used to build a drone detection system, which can aid in preventing threatening situations where the security of people or facilities can be compromised, such as flying over restricted areas in airports or crowds in cities. Common birds appearing in the dataset are the rook (Corvus frugilegus) and the western jackdaw (Coloeus monedula) of the crow family (Corvidae), the European herring gull (Larus argentatus), the common gull (Larus canus) and the black-headed gull (Chroicocephalus ridibundus) of the Laridae family of seabirds. 8600 Rockville Pike To help in counteracting the mentioned issues and allow fundamental studies with a common public benchmark, we contribute with an annotated multi-sensor database for drone detection that includes infrared and visible videos and audio files. Since the servos have shown a tendency to vibrate when holding the platform in specific directions, a third channel of the servo controller is also used to give the possibility to switch on and off the power to the servos using a small optoisolated relay board. The IRcam has two output formats, a raw 320256 pixels format (Y16 with 16-bit greyscale) and an interpolated 640512 pixels image in the I420 format (12 bits per pixel). Overall, the video dataset contains 650 videos (365 IR and 285 visible, of ten seconds each), with a total of 203328 annotated frames. or The fish-eye lens camera is used to feed a foreground/background detector that produces binary masks of moving objects. DRONE_001.wav, HELICOPTER_001.wav, BACKGROUND_001.wav, etc. For example, ImageNet 3232 A Dataset for Multi-Sensor Drone Detection". The data has been obtained at three airports in Sweden: Halmstad Airport (IATA code: HAD/ICAO code: ESMT), Gothenburg City Airport (GSE/ESGP) and Malm Airport (MMX/ESMS).