Convolutional neural networks on small unmanned aerial systems

Kaster, Joshua M; Patrick, James A.; Clouse, Hamilton Scott

doi:10.1109/naecon.2017.8268760

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Al. [6] Convolutional Neural Networks (CNN) on small un-manned aerial systems, review together hardware and convolutional neural network for small unmanaged arial system. Basically it proposed a data and coding approach during the feature extraction and how it selects best feature with the help of various convolutions.…”

Section: Literature Surveymentioning

confidence: 99%

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Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network

Patil¹,

Purohit²

2019

IJEAT

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Memory management is very essential task for large-scale storage systems; in mobile platform generate storage errors due to insufficient memory as well as additional task overhead. Many existing systems have illustrated different solution for such issues, like load balancing and load rebalancing. Different unusable applications which are already installed in mobile platform user never access frequently but it allocates some memory space on hard device storage. In the proposed research work we describe dynamic resource allocation for mobile platforms using deep learning approach. In Real world mobile systems users may install different kind of applications which required ad-hoc basis. Such applications may be affect to execution performance of system as well space complexity, sometime they also affect another runnable applications performance. To eliminate of such issues, we carried out an approach to allocate runtime resources for data storage for mobile platform. When system connected with cloud data server it store complete file system on remote Virtual Machine (VM) and whenever a single application required which immediately install beginning as remote server to local device. For developed of proposed system we implemented deep learning base Convolutional Neural Network (CNN), algorithm has used with tensorflow environment which reduces the time complexity for data storage as well as extraction respectively.

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Section: Literature Surveymentioning

confidence: 99%

“…[7] CBinfer: This work basically propose runtime video data capturing from video streaming. This technique has illustrated in [4] and [6]. System creates the multiple video files from video surveillance system and detects the objects in respective frames.…”

Section: Literature Surveymentioning

confidence: 99%

Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network

Patil¹,

Purohit²

2019

IJEAT

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“…The study provided data transfer to the main control center by communicating with wireless sensors on the agricultural land using a wireless network system with an UAV (Polo et al , 2015). Kaster et al , while using the unloading area of a small unmanned aerial system, performed vehicle detection in real-time with the Faster R-CNN algorithm, an adequate aerial image data set, supercomputers and a dedicated team (Kaster et al , 2017). It has been observed that the use of image processing methods in UAVs has gradually increased in recent years.…”

Section: Introductionmentioning

confidence: 99%

Image processing methods decision mechanism for surveillance applications with UAVs

Ayar

Dalkıran

Kale

et al. 2022

AEAT

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Purpose The use of unmanned aerial vehicles (UAVs) has significantly increased in the past decade and nowadays is being used for various purposes such as image processing, cargo transport, archaeology, agriculture, manufacturing, health care, surveillance and inspections. For this reason, using the appropriate image processing method for the intended use of UAVs increases the study’s success. This study aims to determine the most suitable one among the innovative methods that constitute the image processing system for a UAV to be used for surveillance purposes. Design/methodology/approach Analytical hierarchy process has been used in the solution of the decision problem to be handled in three stages, namely, platform, architecture and method. The most suitable alternative and the effect weights of these criteria results were determined at each stage. Findings As a result of this study, Jetson TX2 was determined as the most suitable embedded platform, ResNet is the optimum architecture and Faster R-convolutional neural networks was the best method in the image processing layer for a system that will provide surveillance with image processing method using UAV. Practical implications In UAV designs, where multiple hardware and software choices and system combinations exist, multi-criteria decision-making (MCDM) approaches can be used as a system decision mechanism. Originality/value The novelty of this work comes from the application of MCDM methods that are used as a multi-layered decision mechanism in UAV design.

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A Comparative Assessment of Deep Neural Network Models for Detecting Obstacles in the Real Time Aerial Railway Track Images

Rampriya

Suganya²,

Nathan³

et al. 2022

Applied Artificial Intelligence

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Convolutional neural networks on small unmanned aerial systems

Cited by 6 publications

References 13 publications

Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network

Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network

Image processing methods decision mechanism for surveillance applications with UAVs

A Comparative Assessment of Deep Neural Network Models for Detecting Obstacles in the Real Time Aerial Railway Track Images

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