Review on Finding Enemy Target using Drones

Abstract

Many industries, including agriculture, aerial photography, surveillance, and the military, employ drone technology. The act of continuously monitoring a circumstance, an area, or a person is known as surveillance. In a military setting where there is extensive observation, this typically occurs. Conflict zones and hostile territories are crucial for the military. the nation's security Transporting personnel often close to susceptible areas allows for the surveillance of people. Watch for any adjustments. In this study, we suggest building a drone with an advanced thermal camera for persons detection. Neural networks may be used to build an image surveillance system. The drone's dual-spectrum thermal imaging camera can operate in total darkness. without any outside lighting, in the dark, and when there is moisture from rain or fog. The drone uses RFID technology to distinguish between local military personnel and outsiders. This article will detect enemy targets and assess their geographic locations using image processing techniques and GPS technology. Three processing units are utilized to regulate the operations of the proposed application. The first process uses thermal camera pictures and digital images to detect opponents nearby, and the second process employs RFID tags to distinguish enemies from friendly troops. Then in the third stage, the enemy's geolocation was obtained using GPS technology in order to pinpoint their location, capture them, and launch an attack. Additionally, opponent identification was done using shape algorithms.

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Review on Finding Enemy Target using Drones
RGS Deemantha1
<37-ce-5975@kdu.ac.lk>
Abstract— Many industries, including agriculture, aerial
photography, surveillance, and the military, employ
drone technology. The act of continuously monitoring a
circumstance, an area, or a person is known as
surveillance. In a military setting where there is
extensive observation, this typically occurs. Conflict
zones and hostile territories are crucial for the military.
the nation's security Transporting personnel often close
to susceptible areas allows for the surveillance of people.
Watch for any adjustments. In this study, we suggest
building a drone with an advanced thermal camera for
persons detection. Neural networks may be used to build
an image surveillance system. The drone's dual-
spectrum thermal imaging camera can operate in total
darkness. without any outside lighting, in the dark, and
when there is moisture from rain or fog. The drone uses
RFID technology to distinguish between local military
personnel and outsiders. This article will detect enemy
targets and assess their geographic locations using image
processing techniques and GPS technology. Three
processing units are utilized to regulate the operations
of the proposed application. The first process uses
thermal camera pictures and digital images to detect
opponents nearby, and the second process employs
RFID tags to distinguish enemies from friendly troops.
Then in the third stage, the enemy's geolocation was
obtained using GPS technology in order to pinpoint
their location, capture them, and launch an attack.
Additionally, opponent identification was done using
shape algorithms.
Keywords— enemy, friendly force, RFID,GPS, Drone
I. INTRODUCTION
The term "observation operations" or "reconnaissance
operations" refers to actions taken to gather information
about an adversary or potential enemy's movements and
resources using visual perception or other location
techniques, or to gather data on the meteorological,
hydrographic, or geological features of the area as well as
the native population. Instead of implying specialization,
observation inherently depends on the human dynamic. A
way of obtaining information with a clear objective is called
reconnaissance. It is conducted before to, during, and
following other operations to provide information for the
intelligence preparation of the battlefield (IPB) process and
enable the commander to plan, confirm, or modify his
course of action. The four different forms of reconnaissance
are route, zone, area, and forceful reconnaissance. The
fields of computer science, information technology, and
computer engineering today significantly contribute to the
ease with which the global military industry operates.
Numerous advancements were made, including UAVs,
Roberts vehicles, unmanned aerial systems, and military
drones. Among the technologies employed are mostly
image processing methods for locating hostile targets,
including form detection algorithms, point detection
algorithms, and others. and the use of GPS coordinates to
determine the precise target locations and map them. There
were a lot of restrictions when using digital photos to
identify opponents; to get around them, thermal image
processing methods can be utilized.
When it comes to military drone technology, the Sri Lankan
troops are rather new to it. Until recently, Sri Lankan armed
forces conducted reconnaissance using highly antiquated
and conventional techniques, such as deploying small
reconnaissance teams into enemy territory, evaluating
targets using grids on maps and aerial photos, etc. However,
there are several benefits to deploying drone technology,
including the protection of soldiers' lives and assistance in
precisely identifying targets. The Sri Lanka Army of 2020
will need devoted R&S troops with adequate numbers,
mobility, firepower, and protection to close with and
overthrow the adversary, battle for information, and carry
out efficient reconnaissance and security operations.
However, our force still lacked the skills necessary to
effectively use drones for reconnaissance and surveillance,
and they continued to conduct analyses and make decisions
using their human brains. In contrast, the armies of the
United States and the United Kingdom developed software
that allowed them to make decisions, identify enemy targets,
and analyze their terrain using drones. This suggested image
processing and machine learning based drone application
will assist in analyzing the landscape of the front line in the
war field and while flying the drone may identify the
opponents, their weapons and their shelters. This drone
application's primary goal is to precisely identify
adversaries, their weapons, and their positions in order to
conduct a successful reconnaissance and win the war.
II. LITERATURE REVIEW
Drone technology is used to monitor and detect criminal
activities in deserts, untamed tribal regions, and
battlegrounds. Operators fly drones that are controlled from
a central place. We were able to determine our location by
using a covert satellite network. The situation is under the
operator's control. drone use for targeting and surveillance.
Present-day drone aircraft take pictures and videos for
surveillance reasons. Massive volumes of monitoring data
are stored in some locations, making access difficult. to
quickly locate a target's exact location within a big quantity
of data after analyzing it

In essence, the drone uses image processing and machine
learning algorithms to recognize and identify adversaries
and their target. So the most common strategy for
identifying an object is to look at its form. One researcher
conducted study to lower the rate of street crimes by using
drone monitoring, and as a result, they employed shapes
identification algorithms to identify the tools and weapons
they used to commit the crimes. We used a shape detection
algorithm to identify weapons or people carrying weapons
at a crime scene. The crime scene is made up of many photos
with different items, yet the form detection algorithm still
manages to discover things there. For storing the
geometrical shape of weapons for detection, we supply
input images and to identify the specific weapon, the
algorithm compares the geometry of new input photos to a
predetermined database. If similar-looking weapons are
made legal with the appropriate permissions granted to
civilians, it will be simpler to identify illicit weapons. This
will contribute to a decrease in crime.
The challenge of identifying simple repeated and precise
motions has received a lot of recent attention in research on
human undertaking awareness. However, a lot of intriguing
human activities include a complicated temporal
composition of simple acts. A thorough understanding of
the temporal structures can help with automatic cognizance
of such complicated events. One researcher used the
temporal organization of human activities to build a
framework for describing motion. In this approach,
researchers represent activities as temporal compositions of
motion segments and train a discriminative model that
encodes a temporal decomposition of video sequences as
well as appearance models for each motion segment. In
recognition, a query video is matched to the model using
motion phase decomposition and learning appearances.
Classification is generally dependent on how well the
temporal segments in the question sequence and the motion
segment classifiers match each other. We provide a fresh
dataset of challenging Olympic Sports events to evaluate
our methodology. We demonstrate that our approach
outperforms existing state-of-the-art methods.Thermal
cameras can detect an infrared source.
A general and reliable method for the real-time recognition
of people and vehicles from an Unmanned Aerial Vehicle
(UAV) is crucial for the deployment of entirely autonomous
UAVs for aerial reconnaissance and surveillance. We
propose an analogous approach for person detection in
thermal pictures, based on a similar cascaded classification
method with added multivariate Gaussian matching. This
demonstrates that, with little false positive detection, cars
and people may be found in a range of contexts, including
remote rural areas and densely populated cities. By
attempting to identify each object of interest at least once in
the environment, the detector's performance is enhanced to
decrease the overall false positive rate rather than trying to
detect every object in each image frame.
With the exception of the fact that object detection does not
require a clear line of sight, RFID technology is similar to
barcode technology. The chip-less tag was used in the game.
Cost-saving is an objective of RFID technology. As revenue
grows, so go the price, the size of the tag, and the amount of
bits. This study demonstrates the use of a resonator to create
chipless tags. A spiral resonating structure is employed for
ultra-high frequency (UHF). Stop band filter response is
accessible at 2.6 GHz. The realized tag is simulated, and the
experimentation's outcomes are evaluated. This research
focuses on the creation of a robust moving object detection
method using thermal and visible spectrum imaging.
Combining visible spectrum and thermal imaging can
provide more information about the moving object since
they are inherently complimentary to one another. Moving
objects are segmented using the "Background Subtraction"
method in connected video frames. Each visible and thermal
pixel is processed as a combination of Gaussians for
background removal, and a robust moving object
identification system is created using blob analysis and the
fusion rule. The researchers have introduced a novel,
straightforward thermo-visible fusion approach for moving
object recognition. Gaussian mixtures are used in the
algorithm's fusion rule. The method is explained for
spotting moving objects in surveillance camera video feeds.
A system using a standard video camera and a thermal
camera placed near to one another is used to identify objects.
First, background removal is carried out separately in each
of the two video streams using the well-known Gaussian
Mixture Models technique. The authors provide an
algorithm for the next processing stage that aligns the
pictures appropriately and projectively modifies the video
streams to synchronize them. The algorithm then combines
the partial background subtraction outputs from the two
cameras to provide a single result, which can be composed
of linked components showing the removal of moving
objects. Testing of the proposed method demonstrates the
utility of the dual algorithm.
III. METHODOLOGY
Currently, the Sri Lanka Army has developed a drone
regiment, but the goal of this research is to offer a solution
for automating the reconnaissance process in the Sri Lankan
Military and to deploy drone technology to the Sri Lankan
Military Special Operation troops. Several databases,
including Google Scholar, Research Gate, and IEE, among
others, were utilized to find sources for this literature study.
Beginning with a simple Google search to learn about the
applications already built for surveillance drones, the
researcher discovered some of the ones described in the
literature study. Secondary researchers have looked into
how items are detected by technology and what kinds of
visual pictures or cameras are employed to record data for
analysis. In light of that, the researcher advises using both
thermal and digital cameras for better results. Then, in order
to better address their concerns, the researcher gathered
many strategies that were employed in various apps and
unified them into one application. The researcher made
several observations about the military to learn about the
practices that are currently used to conduct reconnaissance

and surveillance during military operations. The researcher
then used these observations to focus his research question
and find solutions from this proposed application.
In the initial stage, the drone camera scans the ground using
thermal and digital formations to detect objects in the
ground. Once the camera detects an object, it is processed
using image processing and machine learning algorithms
because inside the drone there is an encryption method. As
a result, it will encrypt the video with the radio frequency
and pass it to the controller before decrypting it from the
application and feeding it into the algorithm. The drone's
CPU is utilized to convert the video data from the thermal
camera into picture frames. Then Algorithm was employed
to detect the item. It will receive praise. The CPU is utilized
to do all of these activities. It receives the information from
the Air Flight Controller, another source, a controller that
obtains RFID data from troops, as well as from the Station's
Base. For our system, we choose to employ a single-board
computer since it should have a more potent CPU and
greater RAM memory storage but with a tiny footprint.
The ability to easily locate the target or target area location
and to plot that location into a digital area map is one of the
benefits of GPS technology. Thermal cameras and an FPV-
based flying system are used to analyse the drone's data. The
range of wireless transmission for streaming video is
constrained by a number of reasons. Route loss will weaken
the signal as the distance between the transmitter and the
receiver grows, and obstructions in the line of sight will
increase attenuation and delay spread. Additionally, there
are two major issues with sending and receiving data via
wireless connections. We employ directional antennas with
a narrow beam and high directional gain, which will
strengthen the drone's received signal and prevent
interference. To steer the antenna toward the moving drone,
an additional tracker can be attached to the antenna. Having
a directed focus at line of sight, which is possible with
directional antenna systems, is the most obvious method to
get rid of reflected signals. The reflected signals will be
attenuated and enter the antenna at an angle outside the main
lobe. Additionally, we developed a superior mode by
leveraging antenna polarisation, which increases the
intensity of the signal delivered and lowers latency and
delay spread, in order to increase efficiency and prevent
multipath fading.
IV. DISCUSSION
To find people carrying weapons in a place. Using a form
detecting technique, the researcher In a battlefield, the form
detection algorithm discovers items; nevertheless, drone
surveillance consists of several photos and videos with
different objects. For storing the geometrical shape of
weapons for detection, researchers submit input pictures.
The system compares the shape of fresh input images to a
prepared database to identify the particular weapon. Using
yolov3 algorithms will make it simpler to identify a human
carrying a weapon. The FLIR Image Dataset, which is open
source and offered by FLIR Systems, was used to compile
the thermal images. The Darknet YOLO v4 Spatial Pooling
Object Identification Technique, a CNN-based system that
can identify even the smallest photographed objects, is used
to allow Human detection in these selected photos. This
results in an accuracy for individuals recognition of around
0.794. The sample picture findings after categorization are
displayed in the photographs below. The person has been
surrounded by blue rectangles. This technique was created
by Google Colab and Thermal Images.
It was also tested at 640x360 and 7 frames per second for
the real-time video feed. To make it easier to identify our
personnel while they are together, we have integrated RFID
technology. The AES encryption used by the RFID tag
guards against data tampering and eavesdropping. This
suggested drone application will provide significant results
that will counteract the researchers' goals and transfer these
technology to Sri Lanka's military.

V. CONCLUTION
The effective use of neural networks for human recognition
in drone surveillance systems for military purposes is
briefly discussed in this paper by researchers. Researchers
have added additional security precautions by using RFID
tags to identify our fighters. Additionally, we believe that
thermal imaging is more dependable and efficient in
surveillance than visible light since it can function in dimly
lit locations, as well as in settings with moisture from rain
and fog and no outside lighting. Finally, the researcher's
goals have been met by the suggested system. In order to
carry out its surveillance duties accurately and effectively
and reduce casualties in the case of a war, the Sri Lankan
Army can benefit from this technique.
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