Optical multi-band detection of unmanned aerial vehicles with YOLO v4 convolutional neural network

2020 ◽  
Author(s):  
Igor S. Golyak ◽  
Dmitriy R. Anfimov ◽  
Igor L. Fufurin ◽  
Andrey L. Nazolin ◽  
Sergey V. Bashkin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
Multi Band

scholarly journals Convolutional neural network target detection in hyperspectral imaging for maritime surveillance

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141984299
Author(s):  
Sara Freitas ◽  
Hugo Silva ◽  
José Miguel Almeida ◽  
Eduardo Silva
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Spectral Angle Mapper ◽  
Maritime Surveillance ◽  
Aerial Vehicles ◽  
Spectral Angle

This work addresses a hyperspectral imaging system for maritime surveillance using unmanned aerial vehicles. The objective was to detect the presence of vessels using purely spatial and spectral hyperspectral information. To accomplish this objective, we implemented a novel 3-D convolutional neural network approach and compared against two implementations of other state-of-the-art methods: spectral angle mapper and hyperspectral derivative anomaly detection. The hyperspectral imaging system was developed during the SUNNY project, and the methods were tested using data collected during the project final demonstration, in São Jacinto Air Force Base, Aveiro (Portugal). The obtained results show that a 3-D CNN is able to improve the recall value, depending on the class, by an interval between 27% minimum, to a maximum of over 40%, when compared to spectral angle mapper and hyperspectral derivative anomaly detection approaches. Proving that 3-D CNN deep learning techniques that combine spectral and spatial information can be used to improve the detection of targets classification accuracy in hyperspectral imaging unmanned aerial vehicles maritime surveillance applications.

scholarly journals Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles

2017 ◽  
Vol 11 (04) ◽  
pp. 1 ◽  
Author(s):  
Jin Gwan Ha ◽  
Hyeonjoon Moon ◽  
Jin Tae Kwak ◽  
Syed Ibrahim Hassan ◽  
Minh Dang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fusarium Wilt ◽  
Deep Convolutional Neural Network ◽  
Aerial Vehicles

scholarly journals Classification of UAVs' distorted images using Convolutional Neural Networks

2020 ◽  
Author(s):  
Leandro Silva ◽  
Jocival D. Júnior ◽  
Jean Santos ◽  
João Fernando Mari ◽  
Maurício Escarpinati ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Convolutional Neural Networks ◽  
Precision Agriculture ◽  
Aerial Vehicles ◽  
Promising Area

Currently, the use of unmanned aerial vehicles (UAVs) is becoming ever more common for acquiring images in precision agriculture, either to identify characteristics of interest or to estimate plantations. However, despite this growth, their processing usually requires specialized techniques and software. During flight, UAVs may undergo some variations, such as wind interference and small altitude variations, which directly influence the captured images. In order to address this problem, we proposed a Convolutional Neural Network (CNN) architecture for the classification of three linear distortions common in UAV flight: rotation, translation and perspective transformations. To train and test our CNN, we used two mosaics that were divided into smaller individual images and then artificially distorted. Results demonstrate the potential of CNNs for solving possible distortions caused in the images during UAV flight. Therefore this becomes a promising area of exploration.

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

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