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14 results for “Search and Rescue”
Search-and-Rescue From Drones With Computer Vision
<p>Unmanned aerial vehicles (UAVs), most commonly known as drones, are increasingly used as a technological support tool for search-and-rescue (SAR) operations (and post-disaster area explorations as well). UAVs equipped with high-resolution cameras and embedded, yet powerful GPUs, in fact, can provide an effective and efficient aid to emergency rescue operations, mainly because locating victims, which may be unconscious or injured, as much fast as possible, is crucial to improve their chance of survival. In particular, the use of drones that are able to automatically detect people in the scenes can increase detection rate, while reducing rescue time. In this repository, we provide a new dataset specifically conceived for SAR operations from drones with computer vision. As it is small-sized, the dataset is currently intended for testing and evaluation purposes only. The main aim of the repository is to encourage contributions on this intriguing topic. In particular, any contribution to make the dataset bigger is welcome.</p>
Data: Search and Rescue with Airborne Optical Sectioning
<p>This dataset supports the finding of our study "Search and Rescue with Airborne Optical Sectioning".</p> <p>Abstract: We show that automated person detection under occlusion conditions can be significantly improved by combining multi-perspective images before classification. Here, we employed image integration by Airborne Optical Sectioning (AOS)---a synthetic aperture imaging technique that uses camera drones to capture unstructured thermal light fields---to achieve this with a recall of 93%. Finding lost or injured people in dense forests is not generally feasible with thermal recordings, but becomes practical with use of AOS integral images. Our findings lay the foundation for effective future search and rescue technologies that can be applied in combination with autonomous or manned aircraft.<br> </p>
Data: Autonomous Drones for Search and Rescue in Forests
<p>Supplementary Dataset for the article Autonomous Drones for Search and Rescue in Forests.</p> <p><strong>Abstract:</strong></p> <p>Drones will play an essential role in human-machine teaming in future search and rescue (SAR) missions. We present a first prototype that finds people fully autonomously in densely occluded forests. In the course of 17 field experiments conducted over various forest types and under different flying conditions, our drone found 38 out of 42 hidden persons; average precision was 86% for predefined flight paths, while adaptive path planning (where potential findings are double-checked) increased confidence by 15%. Image processing, classification, and dynamic flight-path adaptation are computed on-board in real time and while flying. Our finding that deep-learning-based person classification is unaffected by sparse and error-prone sampling within one-dimensional synthetic apertures allows flights to be shortened and reduces recording requirements to one tenth of the number of images needed for sampling using two-dimensional synthetic apertures. The goal of our adaptive path planning is to find people as reliably and quickly as possible, which is essential in time-critical applications, such as SAR. Our drone enables SAR operations in remote areas without stable network coverage, as it transmits to the rescue team only classification results that indicate detections and can thus operate with intermittent minimal-bandwidth connections (e.g., by satellite). Once received, these results can be visually enhanced for interpretation on remote mobile devices.</p>
3rd ACSE Robot Rescue and Search Competition
<p>X. Dai, S.A. Tafrishi, and Y. Kuang. “3rd ACSE Robot Rescue and Search Competition”. University of Sheffield, First Runner-up Team, Master Shifu Robot, May<br /> 2013.</p>
RGB and Thermal Integral Image dataset for Search and Rescue with Airborne Optical Sectioning.
<p>The `Integral Images` folder contains labels and augmented AOS integral images (both RGB and Thermal) used for training, validation and testing (`data`).</p> <p>The integral images are computed using the complete data that were recorded during 18 flights at 6 different sites over 10 different days.</p> <p> </p> <p>The dataset mirrors [YOLO (8GB)](https://zenodo.org/record/3894774/files/YOLO.zip?download=1) (`data`) for integral (`SARAOS/AOS`) images, however, now additionally contain corresponding RGB integral images in addition to corresponding thermal integral images.</p>
First responders' mobile phone traces during a search-and-rescue exercise scenario
<p>This dataset is collected during a search-and-rescue exercise scenario in the framework of the ARTION project. </p> <p>The operation took place on the 22nd of May 2022 in Paphos district (near the beach at Mandria village). The exercise was organized and conducted by the Cyprus Civil Defence and data collection was performed by the KIOS Research and Innovation Center of Excellence of the University of Cyprus. </p> <p>The data is saved in an .xlsx file. It consists of 9 first responders' traces captured during a search-and-rescue operation. The responders were moving on foot holding their mobile phones, which were used for capturing their traces. By means of the ARTION mobile app, the locations of the mobile phones were captured by the build-in GPS receiver of the phone approximately every 5 seconds. </p> <p> </p> <p> </p>
An Experimental Dataset for Search and Rescue Operations in Avalanche Scenarios Based on LoRa Technology
<div><strong>Overview</strong>:</div> <div>The dataset contains measurements of Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) collected from Long-Range (LoRa) devices in avalanche Search and Rescue (SAR) scenarios. Data were collected on a plateau located in Col de Mez (Falcade, Italy) at 1870 m in the Italian Dolomites, at two different times of the year: March and April 2024. The depth and conditions of the snow are different: in March, the snow is mostly dry and over one meter deep, while in April, the snow is wetter, with a greater presence of liquid water, and approximately 55 centimeters deep.</div> <div> </div> <div>The dataset includes three test typologies:</div> <div> <ol> <li>Cross test: 1 buried transmitter, at different depths, and 4 receivers on a tripod, positioned at 10 different distances from the burial point along 4 orientations: North, South, East, West. Distances are: 0.6 m, 1.2 m, 1.8 m, 3 m, 5 m, 10 m, 20 m, 30 m, 40 m, 50 m.</li> <li>Maximum Distance test: 1 buried transmitter and 1 receiver, held in hand and moved away from the burial point until the signal is completely lost. The receiver stops periodically, collecting 2 minutes data in specific markers.</li> <li>Drone Flyover test: 1 buried transmitter and 1 receiver mounted on the bottom of a quadcopter professional drone. The drone stands on 121 measurement points, creating a precise grid covering an area of 100 square meters, with the burial location at the center.</li> </ol> </div> <div>All the tests include precise Ground Truth (GT) annotations, indicating the exact positions of the receivers and the burial depth of the transmitter. The dataset is organized in three folders, one for each test: cross, max_dist and drone. In a separate folder, the snow profiles for the two data collection periods, march and april 2024, are also included, according to the AINEVA Model 4.</div> <div> </div> <div>The dataset aims to assess the ability to locate a victim in an avalanche scenario. The collected data allow for the evaluation of the quality of the LoRa signal in various environmental conditions, as well as the snow depth and snowpack profile. By using precise Ground Truth annotations, it is possible to assess the potential performance of a localization system.</div> <div> </div> <div><strong>How to use the dataset</strong>:</div> <div>Please, read the README file detailing the dataset's format and the data collection campaign. In summary, collected data include:</div> <div> </div> <div>1. Cross test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>rx_pos</li> <li>distance</li> <li>depth</li> <li>polarization</li> </ul> </div> <div>2. Maximum Distance test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>depth</li> <li>id_marker</li> <li>longitude</li> <li>latitude</li> </ul> </div> <div>3. Drone Flyover test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>longitude</li> <li>latitude</li> <li>x</li> <li>y</li> <li>depth</li> </ul> </div> <div><strong>How to cite this dataset</strong>:</div> <div>- DOI number of this datsaset: 10.5281/zenodo.12750580</div> <div>- M. Girolami, F. Mavilia, A. Berton, G. Marrocco and G. Maria Bianco, "An Experimental Dataset for Search and Rescue Operations in Avalanche Scenarios Based on LoRa Technology," in <em>IEEE Access</em>, vol. 12, pp. 171015-171035, 2024, doi: 10.1109/ACCESS.2024.3497654</div>
Drones' footage of a search-and-rescue exercise scenario (at seaside)
<p>This dataset is collected during a search-and-rescue exercise scenario in the framework of the ARTION project.</p> <p>The operation took place on the 13<sup>th</sup> of February 2022 at the seaside of Agios Georgios Pegeias near the city of Pathos. in Cyprus.</p> <p>The exercise was organized and conducted by the Cyprus Civil Defence and data collection was performed by the KIOS Research and Innovation Center of Excellence of the University of Cyprus.</p> <p>The dataset consists of raw video files (.mp4) captured by drones.</p> <p> </p>
Drone-based Thermal Target Tracking with Track Segment Association for Search and Rescue Missions
<p>Supplementary_Video_S1.mp4</p> <p>Supplementary_Video_S2.mp4</p> <p>Supplementary_Video_S3.mp4</p> <p>Supplementary_Video_S4.mp4</p> <p>Supplementary_Video_S5.mp4</p> <p>Supplementary_Video_S6.mp4</p>
First responders' mobile phone traces during a search-and-rescue exercise scenario
<p>This dataset is collected during a search-and-rescue exercise scenario in the framework of the ARTION project. </p> <p>The operation took place on the 11th of April 2021 in an abandoned village (Vretsia) in Paphos district, in Cyprus. <br> The exercise scenario was the following: After a tornado, seven campers who were in the village at the time of the tornado <br> are reported as missing. Some of them are injured. The operations included: establishment and operation of an emergency <br> operation center, first aid station, and drone operation center.</p> <p>For the search-and-rescue operations two rescue teams, a first responder with a rescue dog and a drone were deployed. <br> 1. The rescue dog detects a person. A lightly injured woman is searched and found. The medic team rushes to help <br> and finally accompanies the person to the operation center/first aid station. <br> 2. A victim is identified by the drone and rescued by the Medic Team.<br> 3. The Rescue Team #1 founds an unconscious male and the Medic Team rushes to the rescue. <br> 4. The Rescue Team #2 founds an injured female person and the Medic Team rushes to the rescue.<br> 5. Another person is identified by the drone and rescued by the Medic Team.<br> 6. The Rescue Team #1 founds a lightly injured female person and accompanies her to the first aid station. <br> 7. The Rescue Team #2 founds a lightly injured female person and accompanies her to the first aid station. </p> <p>The exercise was organized and conducted by the Cyprus Civil Defence and data collection was performed by the <br> KIOS Research and Innovation Center of Excellence of the University of Cyprus.</p> <p>The dataset consists of .csv files, each containing a trace, that is, the locations of the mobile phones captured by the built-in<br> GPS receiver of the phone approximately every one second. The mobile phones were held by first responders during their operation. </p>
Drones' footage of a search-and-rescue exercise scenario
<p>This dataset is collected during a search-and-rescue exercise scenario in the framework of the ARTION project.</p> <p>The operation took place on the 11<sup>th</sup> of April 2021 in an abandoned village (Vretsia) in Paphos district, in Cyprus. The exercise scenario was the following: After a tornado, seven campers who were in the village at the time of the tornado are reported as missing. Some of them are injured. The operations included: establishment and operation of an emergency operation center, first aid station, and drone operation center.</p> <p>For the search-and-rescue operations two rescue teams, a first responder with a rescue dog and a drone were deployed.</p> <ol> <li>The rescue dog detects a person. A lightly injured woman is searched and found. The medic team rushes to help and finally accompanies the person to the operation center/first aid station.</li> <li>A victim is identified by the drone and rescued by the Medic Team.</li> <li>The Rescue Team #1 founds an unconscious male and the Medic Team rushes to the rescue.</li> <li>The Rescue Team #2 founds an injured female person and the Medic Team rushes to the rescue.</li> <li>Another person is identified by the drone and rescued by the Medic Team.</li> <li>The Rescue Team #1 founds a lightly injured female person and accompanies her to the first aid station.</li> <li>The Rescue Team #2 founds a lightly injured female person and accompanies her to the first aid station.</li> </ol> <p>The exercise was organized and conducted by the Cyprus Civil Defence and data collection was performed by the KIOS Research and Innovation Center of Excellence of the University of Cyprus.</p> <p>The dataset consists of raw video files (.mp4) captured by drones.</p>
RescueSpeech: A German Corpus for Speech Recognition in Search and Rescue Domain
<p>Dear User,</p> <p>We are thrilled to introduce our latest release - the <strong>RescueSpeech</strong> audio dataset, comprising authentic German speech recordings obtained from simulated search and rescue (SAR) exercises. The dataset contains manually annotated recordings from native German speakers, which were initially captured at 44.1 kHz and later down-sampled to 16 kHz to obtain a set of mono-speaker-single channel audio recordings. In order to protect the identity of the speakers, their names have been anonymized.</p> <p>The RescueSpeech dataset is divided into two sets, each designed for different tasks: Automatic Speech Recognition (ASR) and Speech Enhancement.</p> <p>1. For the ASR task, the dataset spans a duration of 1 hour and 36 minutes. It comprises a collection of clean-noisy pairs, where the noisy utterances are created by introducing contaminations from five different noise types sourced from the AudioSet dataset. These noise types include emergency vehicle siren, breathing, engine, chopper, and static radio noise. To match the 2412 clean utterances in the dataset, we have synthesized an equal number of corresponding noisy utterances. Additionally, we have provided the noise waveform files used to create the noisy utterances, ensuring transparency and reproducibility in the research community.</p> <p>2. The Speech Enhancement task dataset is larger in size compared to the ASR dataset. The primary objective of this dataset is to facilitate the fine-tuning of speech enhancement models, particularly for the five SAR noise types mentioned earlier: emergency vehicle siren, breathing, engine, chopper, and static radio noise. Given the limited duration of clean audio available (1 hour and 36 minutes), we have synthesized multiple noisy utterances with varying noise types and signal-to-noise ratio (SNR) levels, all derived from a single clean utterance. This augmentation approach allows us to generate a more extensive dataset for speech enhancement purposes while preserving the original speaker distribution.</p> <p>By providing these diverse datasets, we aim to support advancements in ASR and Speech Enhancement research, enabling the development and evaluation of robust systems that can handle real-world scenarios encountered during search and rescue operations.<br> </p>
A UWB Radar and Machine Learning-Based Tool for Detecting Victims Through Foliage in Search and Rescue Operations
<h1>Project Description</h1> <p>During our research in University of West Attica (UniWA) we addressed the problem of victim detection through foliage in Search and Rescue operations. For this purpose, a dataset of respiration signal sessions in the field was collected using a proposed tool consiting of a UWB pulsed radar system, and then these data fed a machine learning tool to enhance FR's operations by providing predictions about human presence behind foliage. In addition, two anemometer sensors were used to record wind data, and a respiration belt was employed to obtain the ground truth measurements about the subject's respiration rate.</p> <p>The setup for each session was the same. The UWB radar [<a href="https://sensorlogic.ai/sensor-products">1</a>] was mounted on tripod facing the foliage, the subject was located behind the foliage wearing a respiration belt [<a href="https://www.zephyranywhere.com/">4</a>] for breath recording. On the same tripod two anemometers [<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-speed-transmitter/">2</a>],[<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-direction-transmitter/">3</a>] were placed so a comprehesive image of the wind condiditon during the session could be obtained. These sensors were connected to a laptop via USB, about 3 meters away. The distance between the tripod and the foliage was fixed at 1 meter. Foliage (mostly bushes and small olive trees) had length varying from 1 to 3 meters and the subject (in case of presence session) was from 0.5 to 3 meters away from the foliage. In total we never exceeded the 9.2 meters range (unambiguous range) limit of the radar.</p> <h1>Dataset Description</h1> <p>The dataset consists of 268 sessions of radar, wind and respiration belt data, of which 141 sessions correspond to human presence and 127 to human absence. Each session has a duration of 150 seconds, thus amounting to approximately 6 hours of data for human presence and approximately 5.5 hours of data for human absence.</p> <h2>Dataset Contents</h2> <p>Each session folder is given an individual name X = posixtime; this name designates the exact time (in posixtime format) when the session was started. For example, in the dataset preview below there can be seen one folder named "1688457913"; this folder corresponds to the measurement session that was initiated exactly on 1688457913 in posixtime format (in this example, X = 1688457913). Furthermore, for the "X" posixtime-named folder, there are the following subfolders and files:</p> <p>1. One subfolder named Workspaces_X, containing:</p> <ul> <li>Files named "<em>Workspace_k.mat</em>", where k the number of the created workspaces containing radar signal recording at 16 FPS.</li> <li>A file named "<em>settings.mat</em>", containing the device settings and the session's distances regarding topology.</li> <li>A file named "<em>windData_original.mat</em>", containing the original data from anemometer sensors saved from the data stream at 4 FPS, provided from a microcontroller followed RS485 protocol.</li> </ul> <p>2. Two files containing the raw data recorded from the respiration belt (only for folders corresponding to human presence and for which a respiration belt was used for obtaining the ground truth measurements of the subject's respiration data.)</p> <ul> <li>The "<em>1_YY_MM_DD_HH_MM_general.csv</em>", contains the timestamp in datetime of the sensor and the Android device, the heart rate estimation, the mean breaths per minute and the included IMU belt sensor measurement.</li> <li>The "<em>1_YY_MM_DD_HH_MM_wave.csv</em>", contains the timestamp in datetime of the sensor and the Android device, and 18 values (FPS) of the strain gauge sensor changes from the respiration belt.</li> </ul> <p>3. A file named "<em>X.xlsx</em>", containing the concatenation of the workspaces of the radar signal.</p> <p>4. A file named "<em>windData_X.csv</em>", containing the synchronized data of anemometer sensors with radar data.</p> <p>5. A file named "<em>BeltWfm_X.xlsx</em>", containing the synchronized data of respiration belt with radar data (only for folders corresponding to human presence and for which a respiration belt was used for obtaining the ground truth measurements of the subject's respiration data).</p> <h1>Proposed Tool COTS components</h1> <ol> <li>SLMX4 UWB pulse radar [<a href="https://sensorlogic.ai/sensor-products">1</a>]</li> <li>Wind Speed [<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-speed-transmitter/">2</a>] and Direction [<a href="https://gr.mouser.com/new/dfrobot/dfrobot-rs485-wind-direction-transmitter/">3</a>] sensors</li> <li>Wind data recording equipment (UART TTL to RS485 Converter, MT3608 DC/DC converter, Arduino)</li> <li>Respiration belt [<a href="https://www.zephyranywhere.com/">4</a>]</li> </ol>
Fatigue in Air Search and Rescue Missions
ClinicalTrials.gov study NCT06253026. IPD Sharing: NO. Countries: 1. Publications: 0.
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