scholarly journals The development of modern automated image processing and transfer systems for agriculture unmanned aerial vehicles

2020 ◽  
Vol 161 ◽  
pp. 01087 ◽  
Author(s):  
Marina Vasileva ◽  
Ilyas Ismagilov ◽  
Alexander Gerasimov
Keyword(s):  
Image Processing ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Crop Yields ◽  
Walsh Functions ◽  
Aerial Vehicles ◽  
Transmission Of Information ◽  
High Resolution Images ◽  
Automated Image Processing

The paper contains results of analytic research of unmanned aerial vehicles using in agriculture. The main problems arising in the creation and subsequent large volumes of high-resolution images real time transfer in unmanned aerial vehicles are highlighted. The Automated image processing and transfer system using new methods of information compression on unmanned aerial vehicles board is proposed. The paper considers the issues of consider the problems of constructing new orderings of Walsh functions and constructing fast compression algorithms in synthesized systems of discrete Walsh functions. For processing and subsequent transmission of information from UAVs recommended to use the fast DWT procedure, it allows for a hardware implementation capable of the real-time conversion performing due to its simplicity. The introduction of the proposed solutions for UAVs in agriculture allows to increase accurasy of electronic cartographic material, to keep electronic records of agricultural operations, to carry out operational monitoring of the crops state and to respond quickly for violations and deviations, to predict crop yields and plan their activities for short-term and long-term prospects.

Automated Image Processing Workflow for Unmanned Aerial Vehicles

2021 ◽  
Author(s):  
Samuel Oswald ◽  
Dries Raymaekers ◽  
Wouter Dierckx ◽  
Dominique De Munck ◽  
Stephen Kempenaers ◽  
...  
Keyword(s):  
Image Processing ◽  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
Automated Image Processing

Design of a New Image Processing System Based on FPGA

2014 ◽  
Vol 1003 ◽  
pp. 216-220 ◽  
Author(s):  
Qi Li ◽  
Yu Yang ◽  
Zhong Ke Li ◽  
Jing Lu
Keyword(s):  
Image Processing ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Target Detection ◽  
Processing System ◽  
Image Processing Algorithm ◽  
Verilog Hdl ◽  
Video Decoding ◽  
Image Processing System ◽  
Aerial Vehicles

According to the unmanned aerial vehicles real-time video image acquiring and target detection requirements, an image processing system was designed based on FPGA and TVP5150A decoder, and the video decoding hardware and software was also designed to meet the demands of unmanned aerial vehicles. An I2C controller was realized to assure the implementation of video decoding process in accordance with the requirements, and an image processing algorithm and applied to the image recognition process. Both of these were completed in FPGA using verilog HDL language. The correction of this image processing system was verified through real-time experiments.

scholarly journals Adaptive Human-Robot Interactions for Multiple Unmanned Aerial Vehicles

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Yixiang Lim ◽  
Nichakorn Pongsarkornsathien ◽  
Alessandro Gardi ◽  
Roberto Sabatini ◽  
Trevor Kistan ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Simulation Environment ◽  
Adaptive Automation ◽  
Cognitive States ◽  
Aerial Vehicles ◽  
Machine Interface ◽  
Multiple Unmanned Aerial Vehicles ◽  
Online Adaptation ◽  
Adaptation Phase

Advances in unmanned aircraft systems (UAS) have paved the way for progressively higher levels of intelligence and autonomy, supporting new modes of operation, such as the one-to-many (OTM) concept, where a single human operator is responsible for monitoring and coordinating the tasks of multiple unmanned aerial vehicles (UAVs). This paper presents the development and evaluation of cognitive human-machine interfaces and interactions (CHMI2) supporting adaptive automation in OTM applications. A CHMI2 system comprises a network of neurophysiological sensors and machine-learning based models for inferring user cognitive states, as well as the adaptation engine containing a set of transition logics for control/display functions and discrete autonomy levels. Models of the user’s cognitive states are trained on past performance and neurophysiological data during an offline calibration phase, and subsequently used in the online adaptation phase for real-time inference of these cognitive states. To investigate adaptive automation in OTM applications, a scenario involving bushfire detection was developed where a single human operator is responsible for tasking multiple UAV platforms to search for and localize bushfires over a wide area. We present the architecture and design of the UAS simulation environment that was developed, together with various human-machine interface (HMI) formats and functions, to evaluate the CHMI2 system’s feasibility through human-in-the-loop (HITL) experiments. The CHMI2 module was subsequently integrated into the simulation environment, providing the sensing, inference, and adaptation capabilities needed to realise adaptive automation. HITL experiments were performed to verify the CHMI2 module’s functionalities in the offline calibration and online adaptation phases. In particular, results from the online adaptation phase showed that the system was able to support real-time inference and human-machine interface and interaction (HMI2) adaptation. However, the accuracy of the inferred workload was variable across the different participants (with a root mean squared error (RMSE) ranging from 0.2 to 0.6), partly due to the reduced number of neurophysiological features available as real-time inputs and also due to limited training stages in the offline calibration phase. To improve the performance of the system, future work will investigate the use of alternative machine learning techniques, additional neurophysiological input features, and a more extensive training stage.

Real–Time Virtual Reality Visualizer for Unmanned Aerial Vehicles

2018 ◽  
pp. 479-495
Author(s):  
Fernando A. Chicaiza ◽  
Cristian Gallardo ◽  
Christian P. Carvajal ◽  
Washington X. Quevedo ◽  
Jaime Santana ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Aerial Vehicles

scholarly journals Application of Auxiliary Classifier Wasserstein Generative Adversarial Networks in Wireless Signal Classification of Illegal Unmanned Aerial Vehicles

2018 ◽  
Vol 8 (12) ◽  
pp. 2664 ◽  
Author(s):  
Caidan Zhao ◽  
Caiyun Chen ◽  
Zeping He ◽  
Zhiqiang Wu
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Classification System ◽  
Outdoor Environment ◽  
Classification Model ◽  
Signal Classification ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Aerial Vehicles ◽  
Discriminative Model

Recently, many studies have reported on image synthesis based on Generative Adversarial Networks (GAN). However, the use of GAN does not provide much attention on the signal classification problem. In the context of using wireless signals to classify illegal Unmanned Aerial Vehicles (UAVs), this paper explores the feasibility of using GAN to improve the training datasets and obtain a better classification model, thereby improving the accuracy of classification. First, we use the generative model of GAN to generate a large datasets, which does not need manual annotation. At the same time, the discriminative model of GAN is improved to classify the types of signals based on the loss function of the discriminative model. Finally, this model can be used to the outdoor environment and obtain a real-time illegal UAVs signal classification system. Our experiments confirmed that the improvements on the Auxiliary Classifier Generative Adversarial Networks (AC-GANs) by limited datasets achieve excellent results. The recognition rate can reach more than 95% in the indoor environment, and this method is also applicable in the outdoor environment. Moreover, based on the theory of Wasserstein GANs (WGAN) and AC-GANs, a more robust Auxiliary Classifier Wasserstein GANs (AC-WGANs) model is obtained, which is suitable for multi-class UAVs. Through the combination of AC-WGANs and Universal Software Radio Peripheral (USRP) B210 software defined radio (SDR) platform, a real-time UAVs signal classification system is also implemented.

scholarly journals Suitability of Unmanned Aerial Vehicles for Cadastral Surveys

2019 ◽  
Vol 19 (1) ◽  
pp. 1-8
Author(s):  
S. Mantey
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicles ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Aerial Vehicles ◽  
High Resolution Images ◽  
The Difference ◽  
Cadastral Surveying ◽  
Difficult Terrain ◽  
Global Navigation Satellite

Cadastral surveys in Ghana often employ well known surveying equipment such as Total Station andGNSSreceivers or a combination of both. These survey techniques are well-established and widely accepted. However, there are limitations in certain areas. In situations where difficult terrain and inaccessible areas and dense vegetation are encountered or when surveyor’s life may be at risk, Unmanned Aerial Vehicles (UAVs) could be used to overcome the limitations of these well-established survey instruments. This research used high resolution images from UAV (DJI Phantom 4) to survey plots within the University of Mines and Technology land area. Coordinates of the boundary points were extracted using Agisoft Photoscan.GNSSreceivers were also used to survey the land and the same boundary point coordinates obtained and compared. This enabled the establishment of accurate ground control points for georeferencing. The coordinates obtained from both UAV andGNSSSurveys were used to prepare cadastral plans and compared. The difference in Northings and Eastings from UAV andGNSSsurveys were +0.380 cmand +0.351 cmrespectively. These differences are well within tolerance of +/-0.9114 m(+/-3 ft) set by the Survey and Mapping Division (SMD) of the Lands Commission for cadastral plans production. This research therefore concludes that high resolution images from UAVs are suitable for cadastral surveying. Keywords: Unmanned Aerial Vehicles, Drones, Global Navigation Satellite Systems, Cadastral Surveys

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