UAV-Based Real-Time Survivor Detection System in Post-Disaster Search and Rescue Operations

Dong, Jiong; Ota, Kaoru; Dong, Mianxiong

doi:10.1109/jmass.2021.3083659

Cited by 87 publications

(23 citation statements)

References 32 publications

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“…A more novel application of Computer vision models is its use in security systems as well as safety oversight networks. The sources presented in this section cover applications in detecting violent assaults [ 12 ] and mining personnel safety [ 3 ] to detecting survivors of severe natural disasters [ 113 ]. Most of these applications make use of RGB video and image cameras to perform detection and recognition.…”

Section: Application Based System Comparisonmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

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Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.

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Section: Application Based System Comparisonmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

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“…Vertical correction values vary significantly as the calibration distances change. The effect of the distance value on the vertical correction is modeled mathematically by examining the data from various distances and is represented by (2). By using this equation, the vertical correction value is prevented from undergoing a large change according to the distance.…”

Section: Figure 2 the Determination Of Calibration Values For The Fus...mentioning

confidence: 99%

“…Detailed information such as dimensions, color, distance, and temperature of the surrounding objects may be obtained by various types of sensors in real-time. In numerous human rescue studies, different types of unmanned vehicles perform the pre-rescue human detection tasks using a single sensor [1,2]. However, the accuracy of the classification algorithm can be boosted using different sensors simultaneously, as in [3].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fusion Method With Thermal and RGB-D Sensor Data for Human Detection

Ozcan

Çetin

2022

IEEE Access

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Human detection methods are widely used in various fields such as autonomous vehicles, video surveillance, and rescue systems. To provide a more effective detection system, different types of sensor data (i.e. optics, thermal, and depth data) may be used together as hybrid information. Fortifying object detection, based on optical data and additional sensor data, such as depth and thermal data, also represents information regarding the distance and temperature of classified objects that can be used for video surveillance, rescue systems, and various applications. In this study, a simple and effective method is introduced to fuse RGB-D and thermal sensor data to achieve a more accurate form of human detection. To accurately combine the sensors, they are physically fixed to each other, and the relationship between them is determined using a novel method. The feature points on the optical and thermal images are extracted and matched successfully using computer vision. The proposed method is completely brand-free, easy to implement, and can be used in real-time applications. Using both thermal and optical data, humans are classified as benefiting from a widely used object detection method. The performance of the presented method is tested with a newly generated dataset. The proposed method boosts human detection accuracy by 5% when compared to the use of only optical data and by 37% when compared to the use of thermal data with COCO Dataset upon YOLOv4 neural network weights. After training with the newly generated dataset, the detection accuracy increases by 18% compared with the best results of single sensor usage.INDEX TERMS Data fusion; human detection; image processing.

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“… Industry and agriculture industry and agriculture use cases benefit from IoD innovation in real-time monitoring in order to provide automated water intervention [ 27 ], crop quality and quantity improvement [ 28 ], and efficient energy deployment in industrial smart grid [ 29 ]; while palliating the negative impacts on the environment and improving cost-effectiveness. Emergency one of the most relevant applications of UAVs involves natural disaster monitoring, such as forest-fire management [ 30 ], rescue missions [ 31 ], and finding missing persons [ 32 ]. In such emergency situations, IoD aims to provide efficient coverage in order to connect victims and injured people and to aid in the rapid solicitation of rescue teams.…”

Section: Introductionmentioning

confidence: 99%

“…Emergency one of the most relevant applications of UAVs involves natural disaster monitoring, such as forest-fire management [ 30 ], rescue missions [ 31 ], and finding missing persons [ 32 ]. In such emergency situations, IoD aims to provide efficient coverage in order to connect victims and injured people and to aid in the rapid solicitation of rescue teams.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review of Blockchain Technology for Internet of Drones Security

et al. 2022

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Internet of Drones (IoD) plays a crucial role in the future Internet of Things due to its important features such as low cost, high flexibility, and mobility. The number of IoD applications is drastically increasing from military to civilian fields. Nevertheless, drones are resource-constrained and highly vulnerable to several security threats and attacks. The use of blockchain technology for securing IoD networks has gained growing attention. To this end, this paper presents a systematic literature review to analyze the current research area regarding the security of IoD environments using the emerging blockchain technology. Forty relevant studies were selected from 129 published articles to answer the identified research questions. The selected studies were classified into three main classes based on blockchain type. Furthermore, a comparison of the reviewed articles in terms of different factors is provided. The research findings show that the blockchain can guarantee fundamental security requirements such as authentication, privacy-preserving, confidentiality, integrity, and access control. Finally, open issues and challenges related to the combination of blockchain and IoD technologies are discussed.

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UAV-Based Real-Time Survivor Detection System in Post-Disaster Search and Rescue Operations

Cited by 87 publications

References 32 publications

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

A Novel Fusion Method With Thermal and RGB-D Sensor Data for Human Detection

A Systematic Literature Review of Blockchain Technology for Internet of Drones Security

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