Field Surveillance UAV

1. FIELD SURVEILLANCE
USING UNMANNED
AERIAL VEHICLE (UAV)
Advisor: Dr. Muhammad Hanif
Co Advisor: Dr. Ahmad Kamal Hassan
SDP TITLE

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2017332
Muhammad Umer
2017299
Muhammad Nabeel Sheikh
2017506
Zaheer Ahmad Khan
2017414
GROUP MEMBERS
Sarib Manzoor Balooch

3. CONTENTS OF PRESENTATION
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FYP TITLE LITERATURE REVIEW PROGRESS SINCE THE
PROJECT WAS STARTED
AIMS AND OBJECTIVES
FACTORS TO BE
CONSIDERED
MODEL DEVELOPMENT
MOTIVATION FOR THE
PROJECT
SUSTAINABLE
DEVELOPMENT GOALS
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7
8
9 FLIGHT AND THRUST
CALCULATION

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MOTIVATION FOR THE PROJECT
1 FUNDED BY PUNJAB POLICE
Punjab Police has agreed to fully sponsor this project
https://seeklogo.com/vector-logo/189513/punjab-police

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MOTIVATION FOR THE PROJECT
https://watchmendailyjournal.com/2020/01/07/teen-
found-dead-bago-city-sugarcane-field/
2 RECENT SURGE IN CRIME
This drone can be used to reduce crime rate by catching criminals red-handed in areas such as:
Grassy Fields
Agricultural Land
Forests

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MOTIVATION FOR THE PROJECT
3 OUTDATED CONVENTIONAL METHOD
The conventional method uses a lot of time and resources and usually fails to catch criminals. By
using a drone for this purpose, we can:
Reduce the amount of workforce required, thereby reducing costs as well.
Drastically reduce the time consumed for this task.

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MOTIVATION FOR THE PROJECT
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MOTIVATION FOR THE PROJECT
CASE STUDIES
Mexico:
In Mexico more than 500 arrests were made using
DJI drone resulting in 30% reduction in crime
China:
In China a fugitive who was on the run for 17 years was
caught by police using drone
https://www.colourbox.com/vector/police-catching-thief-with-drone-vector-22740792

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MOTIVATION FOR THE PROJECT
5 REDUCED RISK
Since the position of the suspects would be known, there would be lesser chances of:
Gunfights
Surprise attacks from the suspects
Injuries and deaths

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Innovation
Goal 9: Industry, Innovation, and Infrastructure
Reduces Crime Rate
Goal 16: Peace, Justice and Strong Institutions
SUSTAINABLE DEVELOPMENT GOALS
2 out of 17 sustainable development goals are being targeted in our project:
https://www.un.org/sustainabledevelopment/sustainable-development-goals/

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OBJECTIVES
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AIM AND OBJECTIVES
The UAV must be able to do autonomous surveillance over the desired area.
It must be able to detect suspicious activities using the thermal camera mounted on it.
It must be able to communicate the Ground Control Station (GCS) about the detected suspicious activities.
AIM
To assist the police by helping them detect suspicious activities in the fields that are difficult to approach

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AUTONOMOUS FIELD MOVEMENT
https://www.youtube.com/watch?v=i0oL5wek-c4

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SUSPICIOUS ACTIVITY DETECTION
ANOMALY DETECTION
POSTURE DETECTION USING KEY POINTS
https://avinton.com/en/services/edge-ai-camera/ https://mc.ai/introduction-to-anomaly-detection/

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GROUND CONTROL STATION COMMUNICATION
Communication using telemetry module
Drone position, logs and statistics
https://www.banggood.com/500mW-3DR-Radio-Telemetry-AirGround-Module-433MHz-915-MHz-For-MWC-APM-PIXHAWK-Pirate-p-982005.html

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AUTONOMOUS
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https://droneii.com/drone-autonomy

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AUTONOMOUS
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https://www.researchgate.net/publication/313329204_Security_Privacy_and_Safety_Aspects_of_Civilian_Drones_A_Survey

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LITERATURE REVIEW
UAV Configuration
UAV Frame Material
UAV Components
Numerous research papers/articles were overviewed, and the extracted information was divided into the following 7
sections:
UAV Thrust To Weight Ratio
UAV Architecture
Detection Algorithm

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LITERATURE REVIEW
Detection Algorithm
The model that we have used in activity detection is YOLO version 5
YOLO algorithms divide all the given input images into the SxS grid system
Grid cells predict the boundary boxes for the detected object

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LITERATURE REVIEW
YOLO Architecture
The YOLO network consists of three main pieces.
1) Backbone - A convolutional neural network that aggregates and forms image features at different granularities.
2) Neck - A series of layers to mix and combine image features to pass them forward to prediction.
3) Head - Consumes features from the neck and takes box and class prediction steps.

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Brushless DC Motor
Electronic Speed Control(ESC)
Flight Controller(Pixhawk)
Thermal Camera
Battery
RF Transmitter & Receiver
Jetson Nano(Companion Computer)
MAJOR UAV COMPONENTS
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LITERATURE REVIEW
Carbon-fiber
UAV FRAME MATERIAL
Quad-copter
UAV CONFIGURATION
The minimum thrust to weight
ratio must be 2.0
UAV THRUST TO WEIGHT
RATIO
[1] Yu, X., Zhang, Y., 2015. Sense and avoid technologies with applications to unmanned aircraft systems
[2] Colomina, I., Molina, P., 2014. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of
photogrammetry and remote sensing, 92, pp.79-97

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Each Motor is connected to the flight controller
(Pixhawk) via ESC (Electronic Speed Controller).
Li-Po battery supplies power to each of these motor via
power distribution board.
Raspberry-Pi/Nvidia Jetson Nano will be used to
provide functionality to camera through Companion
Computing.
UAV ARCHITECTURE
LITERATURE REVIEW

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Rating of each Electronic Component (e.g. Motor, ESC,
Battery, Pixhawk etc.)
FACTORS TO BE CONSIDERED
https://bestdroneforthejob.com/blog/three-great-fpv-racing-drone-kits-assembly-required/
Stability
Camera Placement

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FACTORS TO BE CONSIDERED
Environmental Conditions (e.g. wind, rain)
http://actionsportsconnection.com/breaking-racing-drone/p-20170929-02000_news/
http://marcelinhocinegrafista.blogspot.com/2017/08/que-tal-voar-em-dias-de-chuva-dji.html
Flight Endurance
Electronic Interference of GPS
Precautions of Electronic Components (e.g temperature, loose
connections and short circuit

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PROGRESS SINCE THE PROJECT WAS STARTED
Thorough Literature Review
Research on Deep Neural Network
Prototype Assembly
Prototype Automation

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Deploying Deep Neural Network
Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable
of performing tasks that typically require human intelligence
The term artificial intelligence was coined in 1956
AI automates repetitive learning and discovery through data.
AI adapts through progressive learning algorithm
AI achieves incredible accuracy

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Deploying Deep Neural Network
How our Model is implemented?

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Data Collection and Processing
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https://droneii.com/drone-autonomy https://www.researchgate.net/publication/313329204_Security_Privacy_and_Safety_As
pects_of_Civilian_Drones_A_Survey
 Different set of activities were performed
 This project uses data that was collected using DJI Mavic mini.
 The video was recorded in 4k at 60 frames per second
 The images were extracted out of video at 12 frames per second using Python script
 The pictures lack the positions and labels for the objects. Different activities may also appear on the
same image

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Data Collection and Processing

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Data Collection and Processing
images was done using Computer Vision Annotation tool
The Labelling of mages were labeled into two classes: 1. Normal 2. Suspicious

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PyTorch
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Transfer Learning with PyTorch
Transfer Learning
 Transfer learning is a technique for re-training a DNN model on a new dataset
 less time consuming.
 With transfer learning, the weights of a pre-trained model are fine-tuned according to the requirement of
new dataset
 PyTorch is the machine learning framework that we have employed
 In addition to a camera-based tool for collecting and labeling your own training datasets.

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NVIDIA TensorRT
 High-performance neural network inference
optimizer
 Utilizes NVIDIA exclusive CUDA cores
through GPU accelerated computing

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NVIDIA TensorRT

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Make intelligent decisions during flight
For example, “detect activity and send GPS co-
ordinates to the ground station.
COMPANION COMPUTING
http://brisbaneroboticsclub.id.au/connect-nvidia-nano-to-pixhawk/
Communicatewith ArduPilot on a flight controller using the MAVLink protocol
Gets all the MAVLink data produced by the autopilot (including GPS data)

33. FLIGHT TIME CALCULATION
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https://web.ece.ucsb.edu/~yoga/capstone/static/img/projects/slides/vishawk.pdf
Formula:
DFT=(Battery Capacity * Battery Discharge /AAD)*60
Battery Capacity=10000mAH/1000=5AH
Battery Discharge=80%
AAM=AUM*(Power/Voltage)=12A
DFT=(10*0.8/12)*60=40mins

34. THRUST CALCULATION
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https://web.ece.ucsb.edu/~yoga/capstone/static/img/projects/slides/vishawk.pdf
Thrust To Weight Ratio= 2:1
All Up Weight(AUM)= 2.25kg
Total Thrust=4500g
Thrust Of Each Motor=4500/4=1125g
This much thrust per motor is required to get it off the ground and hover.

35. Thank you!