Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach

The objective of this article is to propose and verify a reliable detection mechanism of multi-rotor unmanned aerial vehicles (UAVs). Such a task needs to be solved in many areas such as in the protection of vulnerable buildings or in the protection of privacy. Our system was firstly realized by standard computer vision methods using the Oriented FAST and Rotated BRIEF (ORB) feature detector. Due to the low success rate achieved in real-world conditions, the machine-learning approach was used as an alternative detection mechanism. The “Common Objects in Context dataset” was used as a predefined dataset and it was extended by 1000 samples of UAVs from the SafeShore dataset. The effectiveness and the reliability of our system are proven by four basic experiments—drone in a static image and videos which are displaying a drone in the sky, multiple drones in one image, and a drone with another flying object in the sky. The successful detection rate achieved was 97.3% in optimal conditions.

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A Study on the Repercussions of the COVID-19 Pandemic in the Mental Health of the Common Public: Machine Learning Approach

Artificial Intelligence and Technologies - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-16-6448-9_23 ◽

2021 ◽

pp. 215-231

Author(s):

Anusha Jayasimhan ◽

Preetiha Jayashanker ◽

S. K. Charanya ◽

K. Krithika

Keyword(s):

Mental Health ◽

Machine Learning ◽

Learning Approach ◽

Machine Learning Approach ◽

The Common

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Automated detection and classification of diabetes disease based on Bangladesh demographic and health survey data, 2011 using machine learning approach

Diabetes & Metabolic Syndrome Clinical Research & Reviews ◽

10.1016/j.dsx.2020.03.004 ◽

2020 ◽

Vol 14 (3) ◽

pp. 217-219 ◽

Cited By ~ 1

Author(s):

Md.Merajul Islam ◽

Md.Jahanur Rahman ◽

Dulal Chandra Roy ◽

Md. Maniruzzaman

Keyword(s):

Machine Learning ◽

Survey Data ◽

Health Survey ◽

Automated Detection ◽

Demographic And Health Survey ◽

Learning Approach ◽

Machine Learning Approach ◽

Health Survey Data

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Source deghosting of coarsely-sampled common-receiver data using a convolutional neural network

Geophysics ◽

10.1190/geo2020-0186.1 ◽

2021 ◽

pp. 1-48

Author(s):

Jan-Willem Vrolijk ◽

Gerrit Blacquiere

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Field Data ◽

Simulated Data ◽

Training Data ◽

Learning Approach ◽

Receiver Domain ◽

Machine Learning Approach ◽

The Common

It is well known that source deghosting can best be applied to common-receiver gathers, while receiver deghosting can best be applied to common-shot records. The source-ghost wavefield observed in the common-shot domain contains the imprint of the subsurface, which complicates source deghosting in common-shot domain, in particular when the subsurface is complex. Unfortunately, the alternative, i.e., the common-receiver domain, is often coarsely sampled, which complicates source deghosting in this domain as well. To solve the latter issue, we propose to train a convolutional neural network to apply source deghosting in this domain. We subsample all shot records with and without the receiver ghost wavefield to obtain the training data. Due to reciprocity this training data is a representative data set for source deghosting in the coarse common-receiver domain. We validate the machine-learning approach on simulated data and on field data. The machine learning approach gives a significant uplift to the simulated data compared to conventional source deghosting. The field-data results confirm that the proposed machine-learning approach is able to remove the source-ghost wavefield from the coarsely-sampled common-receiver gathers.

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