aerial surveillance
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2022 ◽  
Vol 14 (2) ◽  
pp. 293
Author(s):  
Mary Ruth McDonald ◽  
Cyril Selasi Tayviah ◽  
Bruce D. Gossen

Aerial surveillance could be a useful tool for early detection and quantification of plant diseases, however, there are often confounding effects of other types of plant stress. Stemphylium leaf blight (SLB), caused by the fungus Stemphylium vesicarium, is a damaging foliar disease of onion. Studies were conducted to determine if near-infrared photographic images could be used to accurately assess SLB severity in onion research trials in the Holland Marsh in Ontario, Canada. The site was selected for its uniform soil and level topography. Aerial photographs were taken in 2015 and 2016 using an Xnite-Canon SX230NDVI with a near-infrared filter, mounted on a modified Cine Star—8 MK Heavy Lift RTF octocopter UAV. Images were taken at 15–20 m above the ground, providing an average of 0.5 cm/pixel and a field of view of 15 × 20 m. Photography and ground assessments of disease were carried out on the same day. NDVI (normalized difference vegetation index), green NDVI, chlorophyll index and plant senescence reflective index (PSRI) were calculated from the images. There were differences in SLB incidence and severity in the field plots and differences in the vegetative indices among the treatments, but there were no correlations between disease assessments and any of the indices.


Author(s):  
V. Ilienko ◽  
M. Gerashchenko ◽  
A. Los ◽  
O. Sautin ◽  
O. Siryk

Unmanned aerial vehicles (UAVs) allow effective solving the problems of reconnaissance, relaying information on targets to means of fire destruction and striking on any type of object. However, there are many problematic issues regarding the creation of a communication system for remote control of UAV of medium and long range, obtaining video information about reconnaissance objects in real time. The methods allow to estimate the value of the deviation of the carrier frequency of the transmitter of the radio communication channel of unmanned aerial system (UAS) in the normal mode of its operation from the value of the prototype stated by the Developer based on the instrumental measurement of the average or assigned frequency of modulated radio emission. The purpose of the article is to consider the methods, technical means and conditions of instrumental evaluation of the radio emission frequency of UAV‟s radio channels transmitters by means of radio frequency control. This technique defines a set of procedures and rules for instrumental evaluation (measurement) of radio frequency of UAVs prototypes radio transmitters by means of radio frequency control in order to verify compliance of its parameters with the requirements of technical conditions or specifications for experimental aircraft.


2021 ◽  
Vol 6 ◽  
pp. 39-45
Author(s):  
Pavel Kuznetsov ◽  
Dmitry Kotelnikov

Advances in diagnostics and monitoring using aerial surveillance drones can provide a clear overview of the operational status of solar arrays. The authors have developed an automated system for solar power plant intelligent monitoring and maintenance. The mentioned system, which is intended for detecting shades and dust covering cells, consists of a drone and intelligent control system situated on the ground. Preliminary test results have shown that accuracy of faulty parts is 92 % if the weather is clear. In order to assess efficiency of the developed system, the authors have built a mathematical model for counting all contributing factors like a solar power plant’s established capacity, UAV type, computational performance, and weather conditions.


2021 ◽  
Author(s):  
Anthony Oliveira Pinto ◽  
Patrick Marques Ciarelli ◽  
Mario Sarcinelli-Filho
Keyword(s):  

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Franz-Michael Sendner

Purpose For the crews and assets of the European Union’s (EU’s) Joint Operations available today, but a vast area in the Mediterranean Sea to monitor, detection of small boats and rafts in distress can take up to several days or even fail at all. This study aims to outline how an energy-autonomous swarm of Unmanned Aerial System can help to increase the monitored sea area while minimizing human resource demand. Design/methodology/approach A concept for an unattended swarm of solar powered, unmanned hydroplanes is proposed. A swarm operations concept, vehicle conceptual design and an initial vehicle sizing method is derived. A microscopic, multi-agent-based simulation model is developed. System characteristics and surveillance performance is investigated in this delimited environment number of vehicles scale. Parameter variations in insolation, overcast and system design are used to predict system characteristics. The results are finally used for a scale-up study on a macroscopic level. Findings Miniaturization of subsystems is found to be essential for energy balance, whereas power consumption of subsystems is identified to define minimum vehicle size. Seasonal variations of solar insolation are observed to dominate the available energy budget. Thus, swarm density and activity adaption to solar energy supply is found to be a key element to maintain continuous aerial surveillance. Research limitations/implications This research was conducted extra-occupationally. Resources were limited to the available range of literature, computational power number and time budget. Practical implications A proposal for a probable concept of operations, as well as vehicle preliminary design for an unmanned energy-autonomous, multi-vehicle system for maritime surveillance tasks, are presented and discussed. Indications on path planning, communication link and vehicle interaction scheme selection are given. Vehicle design drivers are identified and optimization of parameters with significant impact on the swarm system is shown. Social implications The proposed system can help to accelerate the detection of ships in distress, increasing the effectiveness of life-saving rescue missions. Originality/value For continuous surveillance of expanded mission theatres by small-sized vehicles of limited endurance, a novel, collaborative swarming approach applying in situ resource utilization is explored.


2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Muhammad Javed Iqbal ◽  
Muhammad Munwar Iqbal ◽  
Iftikhar Ahmad ◽  
Madini O. Alassafi ◽  
Ahmed S. Alfakeeh ◽  
...  

It is crucial to ensure proper surveillance for the safety and security of people and their assets. The development of an aerial surveillance system might be very effective in catering to the challenges in surveillance systems. Current systems are expensive and complex. A cost-effective and efficient solution is required, which is easily accessible to anyone with a moderate budget. In aerial surveillance, quadcopters are equipped with state-of-the-art image processing technology that captures detailed photographs of every object underneath. A quadcopter-based solution is proposed to monitor desired premises for any unusual activities, like the movement of persons with weapons and face detection to achieve the desired surveillance. After detection of any unusual activity, the proposed system generates an alert for security personals. The proposed solution is based on quadcopter surveillance and video streaming for anomaly detection in the received video streams through deep learning models. A well-known FasterRCNN algorithm is modified for fast learning with feature reduction in the initial feature extraction step. Five different kinds of CNNs were evaluated for their ability to identify objects of interest in surveillance images. ResNet-50–based FasterRCNN with the highest average precision performed as an excellent solution for threat detection. The average precision of the system is 79% across all categories achieved.


Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 87
Author(s):  
Ketan Kotecha ◽  
Deepak Garg ◽  
Balmukund Mishra ◽  
Pratik Narang ◽  
Vipual Kumar Mishra

Visual data collected from drones has opened a new direction for surveillance applications and has recently attracted considerable attention among computer vision researchers. Due to the availability and increasing use of the drone for both public and private sectors, it is a critical futuristic technology to solve multiple surveillance problems in remote areas. One of the fundamental challenges in recognizing crowd monitoring videos’ human action is the precise modeling of an individual’s motion feature. Most state-of-the-art methods heavily rely on optical flow for motion modeling and representation, and motion modeling through optical flow is a time-consuming process. This article underlines this issue and provides a novel architecture that eliminates the dependency on optical flow. The proposed architecture uses two sub-modules, FMFM (faster motion feature modeling) and AAR (accurate action recognition), to accurately classify the aerial surveillance action. Another critical issue in aerial surveillance is a deficiency of the dataset. Out of few datasets proposed recently, most of them have multiple humans performing different actions in the same scene, such as a crowd monitoring video, and hence not suitable for directly applying to the training of action recognition models. Given this, we have proposed a novel dataset captured from top view aerial surveillance that has a good variety in terms of actors, daytime, and environment. The proposed architecture has shown the capability to be applied in different terrain as it removes the background before using the action recognition model. The proposed architecture is validated through the experiment with varying investigation levels and achieves a remarkable performance of 0.90 validation accuracy in aerial action recognition.


2021 ◽  
Author(s):  
Yoo Jeong Ha ◽  
Minjae Yoo ◽  
Soohyun Park ◽  
Soyi Jung ◽  
Joongheon Kim
Keyword(s):  

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5320
Author(s):  
Hailong Huang ◽  
Andrey V. Savkin

To overcome the limitation in flight time and enable unmanned aerial vehicles (UAVs) to survey remote sites of interest, this paper investigates an approach involving the collaboration with public transportation vehicles (PTVs) and the deployment of charging stations. In particular, the focus of this paper is on the deployment of charging stations. In this approach, a UAV first travels with some PTVs, and then flies through some charging stations to reach remote sites. While the travel time with PTVs can be estimated by the Monte Carlo method to accommodate various uncertainties, we propose a new coverage model to compute the travel time taken for UAVs to reach the sites. With this model, we formulate the optimal deployment problem with the goal of minimising the average travel time of UAVs from the depot to the sites, which can be regarded as a reflection of the quality of surveillance (QoS) (the shorter the better). We then propose an iterative algorithm to place the charging stations. We show that this algorithm ensures that any movement of a charging station leads to a decrease in the average travel time of UAVs. To demonstrate the effectiveness of the proposed method, we make a comparison with a baseline method. The results show that the proposed model can more accurately estimate the travel time than the most commonly used model, and the proposed algorithm can relocate the charging stations to achieve a lower flight distance than the baseline method.


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