Rapid Human Body Detection in Disaster Sites Using Image Processing from Unmanned Aerial Vehicle (UAV) Cameras

Mbaïtiga, Zacharie; Fuji, Satoshi; Minori, Shimoji

doi:10.1109/iciibms.2018.8549955

Cited by 17 publications

(7 citation statements)

References 1 publication

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“…It is difficult to mount these high-end computing models directly on small battery-powered UAVs. Therefore, most of them use precaptured or transmitted images from the UAV and run detection algorithms on the server PC [1,2,3,4,5,6,7,8,9,10,19,21,22,29,30,31,32,33,34].…”

Section: Related Workmentioning

confidence: 99%

“…Vehicle detection has been widely studied as a background research area for various applications, like surveillance systems and traffic systems [1,2,3,4,5,6,7]. Various studies using traditional handcrafted methods have been carried out.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, studies have been conducted to apply a large amount of information obtained from Unmanned Aerial Vehicles (UAVs) to various systems. Representative UAV applications exist in social-safety, surveillance, military, and traffic systems, and the field is increasingly expanding [1,2,3,4,5,6,7]. In this application, vehicle detection, which is detecting the position and size of vehicles in UAV imagery, is very important as background information.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery

Han

Yoo

Kwon

2019

Sensors

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Vehicle detection is an important research area that provides background information for the diversity of unmanned-aerial-vehicle (UAV) applications. In this paper, we propose a vehicle-detection method using a convolutional-neural-network (CNN)-based object detector. We design our method, DRFBNet300, with a Deeper Receptive Field Block (DRFB) module that enhances the expressiveness of feature maps to detect small objects in the UAV imagery. We also propose the UAV-cars dataset that includes the composition and angular distortion of vehicles in UAV imagery to train our DRFBNet300. Lastly, we propose a Split Image Processing (SIP) method to improve the accuracy of the detection model. Our DRFBNet300 achieves 21 mAP with 45 FPS in the MS COCO metric, which is the highest score compared to other lightweight single-stage methods running in real time. In addition, DRFBNet300, trained on the UAV-cars dataset, obtains the highest AP score at altitudes of 20–50 m. The gap of accuracy improvement by applying the SIP method became larger when the altitude increases. The DRFBNet300 trained on the UAV-cars dataset with SIP method operates at 33 FPS, enabling real-time vehicle detection.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery

Han

Yoo

Kwon

2019

Sensors

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“…The CO2 sensor is used effectively to reduce the disturbed zone, while the thermal camera endorses the accurate location of the object [17]. Nowadays the smartphone technology with embedded sensors is used to locate the motions of earthquake as well as human being motions [18][19]. A researcher developed a seismic network using smartphones which increases traditional networks capabilities to detect the devices, distinguishes earthquakes and human activates.…”

Section: Introductionmentioning

confidence: 99%

IOT Based Smart System for Servival Detection in Disasters Zones

2021

IJATCSE

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Smart systems with high architecture and advance technology has been expected to mitigate losses during disasters especially in earthquake zones. An invention of robotic technology helps to search and rescue the survivals in disasters zones but unable to transmit real time information to base station on-time to expedite the rescue operations. The development of IOT based smart system modernize the survival detection technology to minimize the damages. The proposed system has been tested in controlled environment to evaluate the results and performance of system. The system has been installed in various place between different obstacles to verify and validate the survivals detection frequency and real time information dissemination frequency to its cross pounding base station as well as on handheld devices. Moreover, this research article encompasses various results of respiration, movements and heartbeats of the survival in any natural disaster zones.

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“…Under this category of data, large-scale aerial image processed as orthophotography is a useful source of information for many domains. To give some examples of the broad array of applications we can find, there have been systems developed for the detection of coastline changes [2], snow avalanches [3], fires [4], bodies in disaster sites [5], trees [6], seedlings [7], roofs [8], transmission towers [9,10], vehicles [11,12], photovoltaic arrays [13,14], vegetation and buildings [15]. We focus on road detection on aerial images being it an important subject, among other things, due to the need to constantly update road maps.…”

Section: Introductionmentioning

confidence: 99%

Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography

Castillo¹,

Díaz-Álvarez²,

Callejo³

et al. 2020

Preprint

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Photogrammetry involves aerial photography of the earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It’s used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep Learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our system applies grammar guided genetic programming to the search of deep neural network architectures. In this kind of evolutive algorithm all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g. Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state of the art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.

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Rapid Human Body Detection in Disaster Sites Using Image Processing from Unmanned Aerial Vehicle (UAV) Cameras

Cited by 17 publications

References 1 publication

Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery

Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery

IOT Based Smart System for Servival Detection in Disasters Zones

Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography

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