Benchmarking YOLOv5 models for improved human detection in search and rescue missions

Drone or unmanned aerial vehicle(UAV)technology has undergone significant changes.The technology allows UAV to carry out a wide range of tasks with an increasing level of sophistication,since drones can cover a large area with cameras.Meanwhile,the increasing number of computer vision applications utilizing deep learning provides a unique insight into such applications.The primary target in UAV-based detection applications is humans,yet aerial recordings are not included in the massive datasets used to train object detectors,which makes it necessary to gather the model data from such platforms.You only look once(YOLO)version 4,RetinaNet,faster region-based convolutional neural network(R-CNN),and cascade R-CNN are several well-known detectors that have been studied in the past using a variety of datasets to replicate rescue scenes.Here,we used the search and rescue(SAR)dataset to train the you only look once version 5(YOLOv5)algorithm to validate its speed,accuracy,and low false detection rate.In comparison to YOLOv4 and R-CNN,the highest mean average accuracy of 96.9%is obtained by YOLOv5.For comparison,experimental findings utilizing the SAR and the human rescue imaging database on land(HERIDAL)datasets are presented.The results show that the YOLOv5-based approach is the most successful human detection model for SAR missions.

Assessment of Meteorological Threats to the Coordinated Search and Rescue of Unmanned/Manned Aircraft

The architecture and working principle of coordinated search and rescue system of unmanned/manned aircraft,which is composed of manned/unmanned aircraft and manned aircraft,were first introduced,and they can cooperate with each other to complete a search and rescue task.Secondly,a threat assessment method based on meteorological data was proposed,and potential meteorological threats,such as storms and rainfall,can be predicted by collecting and analyzing meteorological data.Finally,an experiment was carried out to evaluate the performance of the proposed method in different scenarios.The experimental results show that the coordinated search and rescue system of unmanned/manned aircraft can be used to effectively assess meteorological threats and provide accurate search and rescue guidance.

Structural Design Study of Air-Dropped Unmanned Maritime Mobile Search and Rescue Platforms

In order to improve the efficiency and safety of search and rescue(SAR)at sea,this paper proposes a kind of emergency rapid rescue unmanned craft(air-dropped unmanned maritime motorized search and rescue platform)that can be delivered by a large transport aircraft.This paper studies the structural design scheme of the platform,and the main scale of the platform,the choice of power system and the impact resistance performance are considered in the design process to ensure its rapid response and effective rescue capability under complex sea conditions.Simulation results show that the platform can withstand the impact of air injection into the water and the shipboard equipment can operate normally under the impact load,thus verifying the feasibility and safety of the design.This study serves to improve the maritime search and rescue system and enhance the oceanic emergency response capability.

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Presently,smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping,e-learning,ehealthcare,etc.Despite the benefits of advanced technologies,issues are also existed from the transformation of the physical word into digital word,particularly in online social networks(OSN).Cyberbullying(CB)is a major problem in OSN which needs to be addressed by the use of automated natural language processing(NLP)and machine learning(ML)approaches.This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks,named SRO-MLCOSN model.The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites.The SRO-MLCOSN model initially employs Glove technique for word embedding process.Besides,a multiclass-weighted kernel extreme learning machine(M-WKELM)model is utilized for effectual identification and categorization of CB.Finally,Search and Rescue Optimization(SRO)algorithm is exploited to fine tune the parameters involved in the M-WKELM model.The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision,recall,and F1-score of 96.24%,98.71%,and 97.46%respectively.

COSPAS-SARSAT系统第二代示位标信号的软件无线电设计实现

遇险示位标是全球卫星搜救系统(COSPAS-SARSAT)的一个重要组成部分。目前遇险示位标大多是第一代窄带示位标,采用固定的调制变频架构,存在集成度低、功能单一、升级需要更换硬件电路等问题。针对这些问题,文中设计了一种基于ZYNQ7000+AD9361软件定义无线电集成架构的第二代遇险信标示位标信号产生电路,其优点是信息内容、信号格式、发射参数可软件灵活定义。文中首先根据COSPAS-SARSAT系统第二代遇险示位标信号的格式规范,设计了扩频、滤波、调制等信号处理流程,仿真验证了信号特性;然后,用ZYNQ7000+AD9361软件定义无线电架构完成了PL和PS设计,实现了符合第二代COSPAS-SARSAT示位标规范的发射信号;最后进行了测试,结果表明该设计方法符合设计要求,有效可行。该设计具有分立器件少、集成度很高、高度可编程性、可灵活实现示位标的在线维护升级和信号转换等优点。

DEVELOPMENT OF A SHAPE-SHIFTING MOBILE ROBOT FOR URBAN SEARCH AND RESCUE

A portable shape-shifting mobile robot system named as Amoeba Ⅱ（A-Ⅱ） is developed for the urban search and rescue application. It is designed with three degrees of freedom and two tracked drive systems. This robot consists of two modular mobile units and a joint unit. The mobile unit is a tracked mechanism to enforce the propulsion of robot. And the joint unit can transform the robot shape to get high environment adaptation. A-Ⅱ robot can not only adapt to the environment but also change its body shape according to the locus space. It behaves two work states including the linear state （named as I state） and the parallel state （named as Ⅱ state）. With the linear state the robot can climb upstairs and go through narrow space such as the pipe, cave, etc. The parallel state enables the robot with high mobility on rough ground. Also, the joint unit can propel the robot to roll in sidewise direction. Two modular A-Ⅱ robots can be connected through jointing common interfaces on the joint unit to compose a stronger shape-shifting robot, which can transform the body into four wheels-driven vehicle. The experimental results validate the adaptation and mobility of A-Ⅱ robot.

Robot SLAM with Ad hoc wireless network adapted to search and rescue environments

An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module act as mobile nodes,with various on-board sensors,Tp-link wireless local area network cards,and Tp-link wireless routers.The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network.The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots.This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments.In post-disaster areas,the network is usually absent or variable and the site scene is cluttered with obstacles.To adapt to such harsh situations,the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage.The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN.Therefore,the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment.Simulations and experiments validate the improved performances of the exploration area coverage,object marked,and loop closure,which are adapted to search and rescue post-disaster cluttered environments.

AModified Search and Rescue Optimization Based Node Localization Technique inWSN

Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,etc.Several issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so on.In spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of anchors.The efficiency of the WSN can be considerably influenced by the node localization accuracy.Therefore,this paper presents a modified search and rescue optimization based node localization technique(MSRONLT)forWSN.The major aim of theMSRO-NLT technique is to determine the positioning of the unknown nodes in theWSN.Since the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the technique.The application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO algorithm.In order to validate the effective node localization performance of the MSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.

Research Review on Marine Search and Rescue

Locating the marine target in a quick and precise way is the crucial point of implementing SAR （search and rescue） at sea, which involves aspects of developing SAR strategy and detects the marine targets. As the effect of marine target detection restricts the SAR result directly, the study has focused on reviewing the previous research about marine target detection, especially dim marine target detection. What＇s more, small target detection under complex sea status is one of the severe challenges which is research＇s hotspot and needs more endeavor. Current research results and future research directions are discussed in the paper. The findings can provide systematic view of implementing maritime search and rescue for field researchers and governors.

Vision and Geolocation Data Combination for Precise Human Detection and Tracking in Search and Rescue Operations

In this paper, a study and evaluation of the combination of GPS/GNSS techniques and advanced image processing algorithms for distressed human detection, positioning and tracking, from a fully autonomous Unmanned Aerial Vehicle (UAV)-based rescue support system, are presented. In particular, the issue of human detection both on terrestrial and marine environment under several illumination and background conditions, as the human silhouette in water differs significantly from a terrestrial one, is addressed. A robust approach, including an adaptive distressed human detection algorithm running every N input image frames combined with a much faster tracking algorithm, is proposed. Real time or near-real-time distressed human detection rates achieved, using a single, low cost day/night NIR camera mounted onboard a fully autonomous UAV for Search and Rescue (SAR) operations. Moreover, the generation of our own dataset, for the image processing algorithms training is also presented. Details about both hardware and software configuration as well as the assessment of the proposed approach performance are fully discussed. Last, a comparison of the proposed approach to other human detection methods used in the literature is presented.

Human Detection by Robotic Urban Search and Rescue Using Image Processing and Neural Networks

This paper proposes a new approach for detecting human survivors in destructed environments using an autonomous robot. The proposed system uses a passive infrared sensor to detect the existence of living humans and a low-cost camera to acquire snapshots of the scene. The images are fed into a feed-forward neural network, trained to detect the existence of a human body or part of it within an obstructed environment. This approach requires a relatively small number of images to be acquired and processed during the rescue operation, which considerably reduces the cost of image processing, data transmission, and power consumption. The results of the conducted experiments demonstrated that this system has the potential to achieve high performance in detecting living humans in obstructed environments relatively quickly and cost-effectively. The detection accuracy ranged between 79% and 91% depending on a number of factors such as the body position, the light intensity, and the relative color matching between the body and the surrounding environment.

A scenario construction and similarity measurement method for navy combat search and rescue

Navy combat search and rescue(NCSAR) is an important component of the modern maritime warfare and the scenario of NCSAR is the basis for decision makers to rely on. According to the core elements in the NCSAR process, the NCSAR scenario structure is constructed from seven perspectives based on the multi-view architecture framework. According to the NCSAR scenarios evolution over time, the NCSAR scenario sequence is analyzed and modeled based on the concept lattice method. Then,the incremental construction algorithm of the NCSAR scenario sequence lattice is given. On this basis, the similarity measurement index of NCSAR scenarios is defined, and the similarity measurement model of NCSAR scenarios is proposed. Finally, the rationality of the method is verified by an example analysis. The NCSAR scenario and similarity measurement method proposed can provide scientific guidance for rapid making, dynamic adjustment and implementation of the NCSAR program, and thus improve the efficiency and effectiveness of NCSAR.

A Wireless Communication Method with Dynamic Adding Nodes for the Underground Search and Rescue Robot

A wireless communication method with dynamic adding nodes for Underground Search and Rescue robot is proposed： fix the address of the controller, add repeater nodes into the net dynamically, and shift the address of the mobile terminal. With this method, the Search and Rescue robot can reach the deeper place of a mine to help rescue and keep in touch with the controller through wireless communication in a single channel, even in a complex laneway where radio wave cannot go through the thick wall. The collision in the process of the two-way multi-hop communication in the single channel will also be resolved by the communication direction priority and response signal mechanism, to enhance the reliability of communication. Finally, a sample is designed and an experiment is conducted to verify the efficiency of the method.

An anti-collision algorithm for robotic search-and-rescue tasks in unknown dynamic environments

This paper deals with the search-and-rescue tasks of a mobile robot with multiple interesting targets in an unknown dynamic environment.The problem is challenging because the mobile robot needs to search for multiple targets while avoiding obstacles simultaneously.To ensure that the mobile robot avoids obstacles properly,we propose a mixed-strategy Nash equilibrium based Dyna-Q(MNDQ)algorithm.First,a multi-objective layered structure is introduced to simplify the representation of multiple objectives and reduce computational complexity.This structure divides the overall task into subtasks,including searching for targets and avoiding obstacles.Second,a risk-monitoring mechanism is proposed based on the relative positions of dynamic risks.This mechanism helps the robot avoid potential collisions and unnecessary detours.Then,to improve sampling efficiency,MNDQ is presented,which combines Dyna-Q and mixed-strategy Nash equilibrium.By using mixed-strategy Nash equilibrium,the agent makes decisions in the form of probabilities,maximizing the expected rewards and improving the overall performance of the Dyna-Q algorithm.Furthermore,a series of simulations are conducted to verify the effectiveness of the proposed method.The results show that MNDQ performs well and exhibits robustness,providing a competitive solution for future autonomous robot navigation tasks.

基于多时相TanDEM-X极化干涉SAR数据的水稻株高反演

水稻株高是水稻本身以及土壤、水文、气象等因素的综合反映,是水稻长势监测的重要指标。准确、高效、大范围的株高反演为水稻品种识别、物候监测、病虫害评估和产量预测等提供了可靠的依据。合成孔径雷达(SAR),具有全天时、全天候、穿透性的优势,成为水稻株高反演的重要手段之一。基于极化干涉测量(PolInSAR)的散射模型的反演算法具有严密的物理模型的支撑及较高的反演精度等特点,成为植被高度反演研究的热点。结合极化干涉SAR技术,构建了一种基于RVoG(Random Volume over Ground)模型的水稻株高反演算法,并利用2015年水稻生长季内9个时相的TanDEM-X极化干涉SAR数据,进行了水稻株高反演试验。首先基于每个时相下的极化干涉SAR数据分别得到8个复相干系数,利用这8个复相干系数在考虑卫星双站模式等情况下进行去相干处理,然后建立适用于水稻田特性的RVoG模型,接着构建基于该模型的水稻株高反演迭代算法,最后对9个时相下的TanDEM-X数据进行研究区的水稻株高反演及精度评定。结果表明,当水稻株高高于0.4m时,该方法的反演结果较好,决定系数(R^2)为0.86,均方根误差RMSE为6.79 cm;当水稻株高较低时(水稻株高小于0.4 m),反演误差在0.1~0.8 m之间,反演结果较差,被明显高估。通过分析认为,基于极化干涉理论,TanDEM-X数据在较好地反映出水稻植株的较大体散射量的前提下,利用所构建的基于RVOG模型的水稻株高反演算法,能够较好地反演株高在0.33~1.2 m的水稻株高。

城市搜索与救援队(USAR)后勤保障能力提升——以中国救援队(CSAR)土耳其地震国际救援任务为例

2023年2月6日,土耳其连续发生2次7.8级地震,中国救援队赴土耳其执行国际救援任务。在为期11天的人道主义救援行动中,中国救援队成功营救出6名幸存者。此次国际救援行动后勤保障工作发挥了重大的作用,保障救援行动的顺利开展,同时发现了存在的问题。本文通过土耳其地震救援行动对后勤保障工作总结了经验和做法,促进保障工作不断改进、完善,并对后勤保障能力提升提出几点建议。

基于城市搜索和救援(USAR)能力标准的消防救援特勤队伍建设思考

2021年国务院办公厅印发了《“十四五”国家应急体系规划》,标志应急管理体系的确立和不断健全,同时也对消防救援队伍综合性救援任务提出了新的要求。消防救援队伍除承担火灾扑救外,抢险救援、社会救助类任务逐年增加,跨区域增援逐渐增多。“特勤”是消防救援队伍的“尖刀”和“拳头”,探索非火警任务下的能力需求和发展方向,努力将消防救援特勤队伍建设成为一支能够应对复杂灾害类型、专业技术过硬的综合性应急救援力量迫在眉睫,本文对提升应对重大突发灾害事件的响应能力,做建设性探讨。

基于改进蚁群算法和共用航段的搜索救援通道规划

针对战斗搜救任务场景中,空中搜索救援等待区至跳伞区之间的往返路径规划问题,提出一种基于改进蚁群算法和共用航段的搜索救援通道规划方法。首先,针对基本蚁群算法易出现局部极值及收敛速度慢等缺点,对启发函数、状态转移策略、信息素更新策略进行了改进。然后,针对单起点多目标点的路径规划问题,提出了设置分航点以生成共用航段的搜索救援通道规划方法,并以总路径长度表征任务准备负荷量与分别规划航路进行对比。实验结果表明,在本文测试环境下,改进蚁群算法规划的路径长度分别缩减15.4%和14.2%,搜索救援通道的任务准备负荷量减少25.4%。所提出的搜索救援通道规划方法有一定的理论和应用价值。

Machine-to-Machine Collaboration Utilizing Internet of Things and Machine Learning

Machine-to-Machine (M2M) collaboration opens new opportunities where systems can collaborate without any human intervention and solve engineering problems efficiently and effectively. M2M is widely used for various application areas. Through this reported project authors developed a M2M system where a drone and two ground vehicles collaborate through a base station to implement a system that can be utilized for an indoor search and rescue operation. The model training for drone flight paths achieves almost 100% accuracy. It was also observed that the accuracy of the model increased with more training samples. Both the drone flight path and ground vehicle navigation are controlled from the base station. Machine learning is utilized for modelling of drone’s flight path as well as for ground vehicle navigation through obstacles. The developed system was implemented on a field trial within a corridor of a building, and it was demonstrated successfully.

水下人-机协同搜救作业人因可靠性分析研究

为分析水下人-机协同搜救作业人因可靠性并识别其关键影响因素,根据水下搜救训练实际和相关研究结论提取人因可靠性影响因素;建立人因可靠性影响因素体系,利用解释结构模型定性分析各影响因素间相关关系,将建立的各影响因素的解释结构模型转换为贝叶斯网络拓扑结构,分别构建潜水员作业、无人遥控潜水器(ROV)作业、人-机协同作业人因可靠性分析贝叶斯网络;通过对3类贝叶斯网络进行推理,揭示影响人因可靠性的关键因素及其依赖关系,并定量分析3种作业模式下的人因可靠性变化。结果表明:人-机协同搜救作业中“误解意图”“通信不畅”“信息不对称”“作业精度不足”“丧失救助能力”等影响因素起到关键作用;当上述因素得到较好控制时,人-机协同搜救作业的人因可靠性将会表现出优于单纯的ROV或潜水员独立施救的效能。研究结果可为水下搜救作业人因可靠性分析与提升提供参考。