scholarly journals The Efficiency of Drones Usage for Safety and Rescue Operations in an Open Area: A Case from Poland

2021 ◽  
Vol 14 (1) ◽  
pp. 327
Author(s):  
Norbert Tuśnio ◽  
Wojciech Wróblewski
Keyword(s):  
Field Tests ◽  
Emergency Situation ◽  
Image Transmission ◽  
Search And Rescue ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Time Of Arrival ◽  
Successful Implementation ◽  
Search System ◽  
Difficult Terrain

The use of unmanned aerial systems (UAS) is becoming increasingly frequent during search and rescue (SAR) operations conducted to find missing persons. These systems have proven to be particularly useful for operations executed in the wilderness, i.e., in open and mountainous areas. The successful implementation of those systems is possible thanks to the potential offered by unmanned aerial vehicles (UAVs), which help achieve a considerable reduction in operational times and consequently allow a much quicker finding of lost persons. This is crucial to enhance their chances of survival in extreme conditions (withholding hydration, food and medicine, and hypothermia). The paper presents the results of a preliminary assessment of a search and rescue method conducted in an unknown terrain, where groups were coordinated with the use of UAVs and a ground control station (GCS) workstation. The conducted analysis was focused on assessing conditions that would help minimise the time of arrival of the rescue team to the target, which in real conditions could be a missing person identified on aerial images. The results of executed field tests have proven that the time necessary to reach injured persons can be substantially shortened if imaging recorded by UAV is deployed, as it considerably enhances the chance of survival in an emergency situation. The GCS workstation is also one of the crucial components in the search system, which assures image transmission from the UAV to participants of the search operation and radio signal amplification in a difficult terrain. The effectiveness of the search system was tested by comparing the arrival times of teams equipped with GPS and a compass and those not equipped with such equipment. The article also outlined the possibilities of extending the functionality of the search system with the SARUAV module, which was used to find a missing person in Poland.

scholarly journals High-Resolution Image Transmission from UAV to Ground Station for Search and Rescue Missions Planning

2021 ◽  
Vol 11 (5) ◽  
pp. 2105
Author(s):  
Vladan Papić ◽  
Petar Šolić ◽  
Ante Milan ◽  
Sven Gotovac ◽  
Miljenko Polić
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Image Transmission ◽  
Search And Rescue ◽  
Aerial Images ◽  
Ground Station ◽  
Transmission Distance ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Future Work

Search and rescue (SAR) missions comprise search for, and provision of aid to people who are in distress or imminent danger. Providing the best possible input for the planners and search teams, up-to-date information about the terrain is of essential importance because every additional hour needed to search a person decreases probability of success. Therefore, availability of aerial images and updated terrain maps as a basis for planning and monitoring SAR missions in real-time is very important for rescuers. In this paper, we present a system for transmission of high-resolution images from an unmanned aerial vehicle (UAV) to the ground station (GS). We define and calculate data rate and transmission distance requirements between the UAV and GS in a mission scenario. Five tests were designed and carried out to confirm the viability of the proposed system architecture and modules. Test results present throughput measurements for various UAV and GS distances, antenna heights and UAV antenna yaw angles. Experimental results from the series of conducted outdoor tests show that the proposed solution using two pMDDL2450 datalinks at 2.4 GHz and a directional antenna on the receiving side can be used for a real-time transmission of high-resolution images acquired with a camera on a UAV. Achieved throughput at a UAV-GS distance of 5 km was 1.4 MB/s (11.2 Mbps). The limitations and possible improvements of the proposed system as well as future work are also discussed.

scholarly journals Analysis of Point Cloud Generation from UAS Images

2014 ◽  
Vol II-1 ◽  
pp. 45-51 ◽  
Author(s):  
S. Ostrowski ◽  
G. Jóźków ◽  
C. Toth ◽  
B. Vander Jagt
Keyword(s):  
Local Coordinate System ◽  
Field Tests ◽  
Point Clouds ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Surface Model ◽  
Geospatial Information ◽  
Software Packages ◽  
The Ohio State University ◽  
Aerial Systems

Unmanned Aerial Systems (UAS) allow for the collection of low altitude aerial images, along with other geospatial information from a variety of companion sensors. The images can then be processed using sophisticated algorithms from the Computer Vision (CV) field, guided by the traditional and established procedures from photogrammetry. Based on highly overlapped images, new software packages which were specifically developed for UAS technology can easily create ground models, such as Point Clouds (PC), Digital Surface Model (DSM), orthoimages, etc. The goal of this study is to compare the performance of three different software packages, focusing on the accuracy of the 3D products they produce. Using a Nikon D800 camera installed on an ocotocopter UAS platform, images were collected during subsequent field tests conducted over the Olentangy River, north from the Ohio State University campus. Two areas around bike bridges on the Olentangy River Trail were selected because of the challenge the packages would have in creating accurate products; matching pixels over the river and dense canopy on the shore presents difficult scenarios to model. Ground Control Points (GCP) were gathered at each site to tie the models to a local coordinate system and help assess the absolute accuracy for each package. In addition, the models were also relatively compared to each other using their PCs.

scholarly journals Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2834
Author(s):  
Billur Kazaz ◽  
Subhadipto Poddar ◽  
Saeed Arabi ◽  
Michael A. Perez ◽  
Anuj Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Pollution Prevention ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Construction Sites ◽  
State Regulations ◽  
Novel Approach ◽  
Model Training ◽  
Aerial Systems

Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices.

Unmanned aerial systems (UAS) in urban search and rescue-methodology, capacity development, and integration

2021 ◽  
Vol 19 (1) ◽  
pp. 33-38
Author(s):  
Ariel Braverman, BSc, RN, EMT-P
Keyword(s):  
Situational Awareness ◽  
Search And Rescue ◽  
Unmanned Aerial Systems ◽  
Urban Search And Rescue ◽  
Emergency Operations ◽  
Ventilation Ducts ◽  
Speed Up ◽  
Modernization Process ◽  
Roadless Areas ◽  
Aerial Systems

This paper’s purpose is to establish a methodological basis for using unmanned aerial vehicles (UAV) in urban search and rescue (USAR). Modern USAR operations involve the location, rescue (extrication), and initial medical stabilization of individuals trapped in confined spaces or places with complicated access, eg, high structures. As a part of the ongoing modernization process, this paper explores possible options for UAV utilization in USAR operations. Today, UAV are already taking part in support emergency operations all over the world, and possible forms of operation for UAV in USAR environment can be in two primary modes: on-site and logistic chain. The on-site mode includes various capabilities of multilayer UAV array, mostly based on enhanced visual capabilities to create situational awareness and to speed-up search and rescue (SAR) process including using nanodrones for entering into confined places, ventilation ducts, and underground sewer channels can give to rescue teams’ opportunities to have eyes within ruins even before initial clearing process. Cargo drones will be able to bring equipment directly to high floors or roadless areas in comparison to wheeled transportation. The advantages of cargo drones operation are the ability of autonomous flight based on GPS or homing beacon and ability to provide logistics supports without involving additional personnel and vehicles and with no dependence on road conditions.

Wireless Robotics Networks for Search and Rescue in Underground Mines

2019 ◽  
pp. 286-309 ◽  
Author(s):  
Alok Ranjan ◽  
H. B. Sahu ◽  
Prasant Misra
Keyword(s):  
Emergency Situation ◽  
Underground Mine ◽  
Search And Rescue ◽  
Underground Mines ◽  
Promising Alternative ◽  
Wireless Robotics ◽  
Research Areas ◽  
Current State ◽  
Challenging Tasks ◽  
Underground Mine Workings

To ensure the safety of miners, reliable and continuous monitoring of underground mine environment plays a significant role. Moreover, such a reliable communication network is essential to provide speedy rescue and recovery operations in case of an emergency situation in a mine. However, due to the hostile nature and unique characteristics of underground mine workings, emergency response communication and disaster management are very challenging tasks. This chapter presents an overview of evolving technology wireless robotics networks (WRN) which may be a promising alternative to support search and rescue (SAR) operation in underground mine emergencies. The chapter first outlines the introduction followed by a detailed discussion on the current state of the art on WRNs and their development in the context of underground mines. Finally, this chapter provides some insights on open research areas targeting the current wireless research design community and those interested in pursuing such challenging problems in this field.

scholarly journals Measuring Transverse Displacements Using Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV): Development and Field Validation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6051
Author(s):  
Piyush Garg ◽  
Roya Nasimi ◽  
Ali Ozdagli ◽  
Su Zhang ◽  
David Dennis Lee Mascarenas ◽  
...  
Keyword(s):  
Laser Doppler Vibrometer ◽  
Field Tests ◽  
Field Measurements ◽  
Cost Effective ◽  
Laser Doppler ◽  
Unmanned Aerial Systems ◽  
Dynamic Displacement ◽  
Railway Bridges ◽  
Industrial Adoption ◽  
Aerial Systems

Measurement of bridge displacements is important for ensuring the safe operation of railway bridges. Traditionally, contact sensors such as Linear Variable Displacement Transducers (LVDT) and accelerometers have been used to measure the displacement of the railway bridges. However, these sensors need significant effort in installation and maintenance. Therefore, railroad management agencies are interested in new means to measure bridge displacements. This research focuses on mounting Laser Doppler Vibrometer (LDV) on an Unmanned Aerial System (UAS) to enable contact-free transverse dynamic displacement of railroad bridges. Researchers conducted three field tests by flying the Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV) 1.5 m away from the ground and measured the displacement of a moving target at various distances. The accuracy of the UAS-LDV measurements was compared to the Linear Variable Differential Transducer (LVDT) measurements. The results of the three field tests showed that the proposed system could measure non-contact, reference-free dynamic displacement with an average peak and root mean square (RMS) error for the three experiments of 10% and 8% compared to LVDT, respectively. Such errors are acceptable for field measurements in railroads, as the interest prior to bridge monitoring implementation of a new approach is to demonstrate similar success for different flights, as reported in the three results. This study also identified barriers for industrial adoption of this technology and proposed operational development practices for both technical and cost-effective implementation.

scholarly journals Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 166-179 ◽  
Author(s):  
Ziyang Tang ◽  
Xiang Liu ◽  
Hanlin Chen ◽  
Joseph Hupy ◽  
Baijian Yang
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Object Detection ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Computer Assisted ◽  
Visual Interpretation ◽  
Two Phase ◽  
Aerial Systems ◽  
Spot Fires

Unmanned Aerial Systems, hereafter referred to as UAS, are of great use in hazard events such as wildfire due to their ability to provide high-resolution video imagery over areas deemed too dangerous for manned aircraft and ground crews. This aerial perspective allows for identification of ground-based hazards such as spot fires and fire lines, and to communicate this information with fire fighting crews. Current technology relies on visual interpretation of UAS imagery, with little to no computer-assisted automatic detection. With the help of big labeled data and the significant increase of computing power, deep learning has seen great successes on object detection with fixed patterns, such as people and vehicles. However, little has been done for objects, such as spot fires, with amorphous and irregular shapes. Additional challenges arise when data are collected via UAS as high-resolution aerial images or videos; an ample solution must provide reasonable accuracy with low delays. In this paper, we examined 4K ( 3840 × 2160 ) videos collected by UAS from a controlled burn and created a set of labeled video sets to be shared for public use. We introduce a coarse-to-fine framework to auto-detect wildfires that are sparse, small, and irregularly-shaped. The coarse detector adaptively selects the sub-regions that are likely to contain the objects of interest while the fine detector passes only the details of the sub-regions, rather than the entire 4K region, for further scrutiny. The proposed two-phase learning therefore greatly reduced time overhead and is capable of maintaining high accuracy. Compared against the real-time one-stage object backbone of YoloV3, the proposed methods improved the mean average precision(mAP) from 0 . 29 to 0 . 67 , with an average inference speed of 7.44 frames per second. Limitations and future work are discussed with regard to the design and the experiment results.

Selected aspects of modelling the system on the example of search and rescue service

2018 ◽  
Vol 120 ◽  
pp. 39-48
Author(s):  
Norbert Chamier-Gliszczyński ◽  
Jerzy Fiuk
Keyword(s):  
Search And Rescue ◽  
Operating Costs ◽  
System Model ◽  
Time Of Arrival ◽  
International Convention ◽  
Rescue Service ◽  
Full Analysis ◽  
Maritime Search And Rescue

The article is an attempt to present the aspect of modelling the system on the example of air maritime rescue interpreted as SAR service (Search and Rescue). The necessity and need for the existence of the SAR service results from the International Convention on Maritime Search and Rescue signed by Poland. An important element of the structure of the SAR service are its individual elements (eg. location of air bases, etc.). The time of arrival in places far away from the currently existing SAR air service bases is so significant that it may cause the failure of the action. The question arises whether increasing the number of airbases gives a chance to reduce the system's operating costs, increase its efficiency and thereby raise the level of safety at sea and in the area of responsibility of the SAR service. For the full analysis of the functioning of the SAR service it is necessary to undertake optimization studies. Due to the complexity of the SAR service, it should be considered in systemic categories. Authors interpreting aviation SAR service in the form of a system whose mapping is the SAR system model undertakes research aimed at optimizing the operation of this service.

scholarly journals First Successful Rescue of a Lost Person Using the Human Detection System: A Case Study from Beskid Niski (SE Poland)

2021 ◽  
Vol 13 (23) ◽  
pp. 4903
Author(s):  
Tomasz Niedzielski ◽  
Mirosława Jurecka ◽  
Bartłomiej Miziński ◽  
Wojciech Pawul ◽  
Tomasz Motyl
Keyword(s):  
Detection System ◽  
Search And Rescue ◽  
Human Detection ◽  
Aerial Images ◽  
Rescue Service ◽  
Se Poland ◽  
System A ◽  
Close Range ◽  
Automated Algorithms

Recent advances in search and rescue methods include the use of unmanned aerial vehicles (UAVs), to carry out aerial monitoring of terrains to spot lost individuals. To date, such searches have been conducted by human observers who view UAV-acquired videos or images. Alternatively, lost persons may be detected by automated algorithms. Although some algorithms are implemented in software to support search and rescue activities, no successful rescue case using automated human detectors has been reported on thus far in the scientific literature. This paper presents a report from a search and rescue mission carried out by Bieszczady Mountain Rescue Service near the village of Cergowa in SE Poland, where a 65-year-old man was rescued after being detected via use of SARUAV software. This software uses convolutional neural networks to automatically locate people in close-range nadir aerial images. The missing man, who suffered from Alzheimer’s disease (as well as a stroke the previous day) spent more than 24 h in open terrain. SARUAV software was allocated to support the search, and its task was to process 782 nadir and near-nadir JPG images collected during four photogrammetric flights. After 4 h 31 min of the analysis, the system successfully detected the missing person and provided his coordinates (uploading 121 photos from a flight over a lost person; image processing and verification of hits lasted 5 min 48 s). The presented case study proves that the use of an UAV assisted by SARUAV software may quicken the search mission.

scholarly journals Automatic Detection of Wrecked Airplanes from UAV Images

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Anhar Risnumawan ◽  
Muhammad Ilham Perdana ◽  
Alif Habib Hidayatulloh ◽  
A. Khoirul Rizal ◽  
Indra Adji Sulistijono ◽  
...  
Keyword(s):  
Visual Information ◽  
Detection System ◽  
Search And Rescue ◽  
Color Variation ◽  
Aerial Images ◽  
Rescue Team ◽  
Access Road ◽  
Uav Images ◽  
Exploration Area ◽  
Accident Site

Searching the accident site of a missing airplane is the primary step taken by the search and rescue team before rescuing the victims. However, due to the vast exploration area, lack of technology, no access road, and rough terrain make the search process nontrivial and thus causing much delay in handling the victims. Therefore, this paper aims to develop an automatic wrecked airplane detection system using visual information taken from aerial images such as from a camera. A new deep network is proposed to distinguish robustly the wrecked airplane that has high pose, scale, color variation, and high deformable object. The network leverages the last layers to capture more abstract and semantics information for robust wrecked airplane detection. The network is intertwined by adding more extra layers connected at the end of the layers. To reduce missing detection which is crucial for wrecked airplane detection, an image is then composed into five patches going feed-forwarded to the net in a convolutional manner. Experiments show very well that the proposed method successfully reaches AP=91.87%, and we believe it could bring many benefits for the search and rescue team for accelerating the searching of wrecked airplanes and thus reducing the number of victims.

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