army target detection using machine learning

DR. SRI. SRI. SRI. SHIVAKUMARA MAHASWAMY COLLEGE
OF ENGINEERING
BYRANAYAKANAHALLI,NELAMANGALATQ.BANGALORERURALDIST.-562132
2022–2023
“MILITANT INTRUSION DETECTION USING
MACHINE LEARNING”
ASHOKA M [1CC19CS007]
RAGHUNATHA T R [1CC19CS032]
PRANAV V S [1CC19CS031]
DRUWA KUMAR C [1CC19CS018]
Presented By Under the Guidance of
Miss. Vinaya D S, B.E, M Tech
Assistant Professor
Department of computer science and Engineering

AGENDA
 Introduction
 Problem Statement
 Literature survey
 Objectives
 Existing System
 Proposed System
 System Design
 High Level Design
 Implementation Modules
 Conclusion
 References

Introduction
When An Individual Carries A Weapon (Firearm Or A Knife) Out In The Open, It Is A Strong Indicator Of A
Potentially Dangerous Situation. While Some Countries Allow For Open Carry Firearms, In Such An Event,
It Is Still Advisable To Grab The CCTV Operators’ Attention In Order To Assess The Situation At Hand.
During Recent Years, An Increase In The Number Of Incidents With The Use Of Dangerous Automated
Methods For Video Surveillance Have Started To Emerge In Recent Years, Mainly For The Purpose Of
Intelligent Transportation Systems (ITS).
We Have Focused On The Specific Task Of Automated Detection And Recognition Of Dangerous Situations
Applicable In General For Any Cctv System. The Problem We Are Tackling Is The Automated Detection Of
Dangerous Weapons—knives And Firearms, The Most Frequently Used And Deadly Weapons. The
Appearance Of Such Objects Held In A Hand Is An Example Of Assign Of Danger To Which The Human
Operator Must Be Alerted

Introduction (CONT….)
Closed Circuit Television Systems (CCTV) Are Becoming More And More Popular And Are
Being Deployed In Many Offices, Housing Estates And In Most Public Spaces. There Are A
Million Of CCTV Cameras That Are Currently In Operation In India. This Makes For An
Enormous Load For The CCTV Operators, As The Number Of Camera Views A Single Operator
Can Monitor Is Limited By Human Factors. The Task Of The CCTV Operator Is To Monitor And
Control, Detect, Observe, Recognize And Identify Individuals And Situations That Are Potentially
Harmful To Other People And Property But It Becomes Harder To Monitor When There Are A
Lot Of CCTV Cameras.
A Solution To The Problem Of Overloading The Human Operatories To Apply Automated Image-
understanding Algorithms, Which, Rather Than Substituting The Human Operator, Alert Them If
A Potentially Dangerous Situation Is At Hand

Problem Statement
To Design And Implement A System To Detect The Weapon And Militant In The Given Image And Answer
The Question Related To Image.
INPUT: Image Containing Weapon And Militant. Process:
• The Processing Consists Of Identification Of The Individual Component Part Of The Weapon
And Militant By Using CNN Algorithm .
• After Identification If Any Weapon And Militants Are Found That Will Be Detected.
OUTPUT: Display Weapon And Militant Type When Weapon And Militant Are Detected It Intimates The
Admin Side Of User.

Objectives
Detection Of Human Image And Criminal Identification
Detection Of Segment Containing The Weapon In The Detected Image Segment Containing Human
Extraction Of Features Of Each Segment
Design Of A Neural Network Based Classifier To Classify A Single Type Of Weapon Verses Non-
weapon
Computation Image Required To Detect The Weapon Directly In A Image As Compared To Detection
Of Weapon After The Human Detection.

Existing System And Drawbacks:
 The Existing Systems Does Not Classify Normal And Abnormal Events Leading The Police To
Become More Reluctant To Attend The Crime Scenes Unless There Was A Visual Verification,
Either By Manned Patrols Or By Electronic Images From The Surveillance Camera.
 The System Is Done With The Image Classification Model Using Cnn With The Concept Of
Sequential Models And Yolov3 Model In The Darknet Framework
Drawbacks:
 The Processing Speed Of The Existing System And Accuracy Will Be Low .
 Running Under The Darknet Framework Which Is Made By C Language
 Background Junk Detections Were High.

Problem Identification
Nowadays Protection For Personal And Personal Property Becoming Very Important. Video Surveillance
Gives A Good Role In Real-time.
Because Of These Needs Deployment Of Cameras Take Place At Every Corner, Video Surveillance System
Understand The Scene And It Automatically Detects Abnormal Activities.
To Recognize The Occurrence Of Uncommon Events Such As Unknown Or With Weapon Or Grenades Or
Tankers Detects In The Low Resolution Video Simply By Using Statistical Property, Standard Deviation
Of Moving Objects.
Detection And Getting Details About The Detection Is Logically Hard To Get Implement With Efficient
Output.There Are Lots Of Techniques To Detect The Different Types Of Anomaly Detection In Surveillance.
Conventional Methods Of Detecting Intrusion In Naked Eye Observation In Live Time Detection.

Proposed System
Here We Proposes A System For Militant And Military Object Detection Using Yolov5 And Python. The
Project Involves Collecting A Large Dataset Of Annotated Images Of Military Objects And Militants,
Splitting The Dataset Into Training, Validation, And Testing Sets, And Converting The Annotated Images
Into Yolov5 Format. The Yolov5 Model Is Then Trained On The Annotated Dataset And Evaluated Using
Various Metrics Such As Precision, Recall, And Map .
Once The Model Is Trained And Evaluated, It Is Deployed To Detect Militants And Military Objects In Real-
time Images Or Videos Using Python Libraries Like Opencv Or Pytorch . The Proposed System Can Also Be
Integrated With Additional Features Such As Object Tracking, Alert Notifications, Or Automatic Response
Mechanisms To Enhance Its Functionality. The Project Demonstrates The Potential Of Deep Learning-based
Object Detection For Military And Defense Applications And Serves As A Basis For Further Research In
This Area.

System Design
The System Architecture For The Proposed System. The Input Image
Is Preprocessed And Converted To Gray Scale Image To Get The
Clear Vision Of The Image. Then It Will Be Converted Into Binary
Values. In The Next Step Identifies The Part Which Needs To
Proceed Further.
Then Required Feature Are Extracted By In The Cnn Convolution
Layer. By Passing Those Features Into Different Layer Of CNN We
Get Compressed Image, That Feature Is Used For Detection Of
Weapon And Militant Using Softmax Activation Function.

Convolutional Neural Network (YOLO)

Yolo Architecture
For Detecting Militant Data Is Trained Using Yolo Model. YOLO, In A Single Glance, Takes The Entire
Image And Predicts For These Boxes The Bounding Box Coordinates And Class Probabilities. Yolo's
Greatest Advantage Is Its Outstanding Pace, It's Extremely Fast, And It Can Handle 45 Frames Per
Second .Amongst The Three Versions Of YOLO 3 And 5, Is Fastest And More Accurate In Terms Of
Detecting Small Objects. The Proposed Algorithm, YOLO Consists Of Total 106 Layers . The
Architecture Is Made Up Of 3 Distinct Layer Forms. Firstly, The Residual Layer Which Is Formed
When Activation Is Easily Forwarded To A Deeper Layer In The Neural Network. In A Residual Setup,
Outputs Of Layer 1 Are Added To The Outputs Of Layer 2. Second Is The Detection Layer Which
Performs Detection At 3 Different Scales Or Stages. Size Of The Grids Is Increased For Detection. Third
Is The Up-sampling Layer Which Increases The Spatial Resolution Of An Image. Here Image Is Up
Sampled Before It Is Scaled. Also, Concatenation Operation Is Used, To Concatenate The Outputs Of
Previous Layer To The Present Layer. Addition Operation Is Used To Add Previous Layers. In The Fig 3,
The Pink Colored Blocks Are The Residual Layers, Orange Ones Are The Detection Layers And The
Green Are The Up-sampling Layers. Detection At Three Different Scales Is As Shown Fig.

Data flow diagram
A Data Flow Diagram (DFD) Is Graphic Representation Of The "Flow" Of Data Through An Information
System.
A Data Flow Diagram Can Also Be Used For The Visualization Of Data Processing (Structured Design).
It Is Common Practice For A Designer To Draw A Context Level Dfd First Which Shows The Interaction
Between The System And Outside Entities.

This Level Of Preprocessing Shows That The Image Is Given As Input.
 As We Giving The Color Image So That Rgb Image Is Converted Into Gray Scale Values To Reduce
Complexity In The Image.
 For Efficient Feature Extraction Gray Scale Values Are Converted Into Binary Values.
Then The Image With Reduced Complexity Is Send To The Next Process.

The Figure Of Identification Shows That The Image With Reduced Complexity Is Considered As Input.
 Here The Region With The Value Of One Is Considered As Black That Region Is Considered For Next
Process.

Shows That The Region Of Interest From The Identification Step Is Considered As Input. The Region Of
Interest Is Obtained From Converting RGB Color Image To The Gray Scale Image By Using Minmax Scalar
Method.
 For That Region Cnn Algorithm Is Applied. A CNN Consists Of An Input Layer And An Output Layer, As
Well As Multiple Hidden Layers Between Them.
The Hidden Layer Basically Consists Of The Convolution Layer, Pooling Layer, Relu Layer And Fully
Connected Layers.

Shows That The One-dimension Array Is Send To Fully Connected Layer Of CNN. Artificial Neural
Network Method Is Applied To This Layer.
Firstly, One-dimension Array Is Sent To Input Layer. Some Particular Feature Which Is Required For The
Detection Is Identified By The Hidden Layer Of ANN.
 The Continue Connection From Hidden Layer To Output Layer Will Help To Identify Accurate Result. By
Considering All The Features Output Layer Gives The Result With Some Predictive Value. These Values Are
Calculated By Using Softmax Activation Function.
Softmax Activation Function Provides Predictive Values. Based On The Prediction Value The Final Result
Will Be Identified. The Highest Value Of Prediction Is Identified As Weapon And Militant.

class diagram
Class Diagram Describes The Attributes And Operations Of A Class And Also The Constraints Imposed On
The System. The Class Diagrams Are Widely Used In The Model Ingo Object Oriented Systems Because
They Are The Only UML Diagrams, Which Can Be Mapped Directly With Object-oriented Languages.
The Purpose Of Fig Is To Model The Static View Of An Application By Using Class Diagram Are The Only
Diagram Which Can Be Directly Mapped With Object–oriented Languages And Thus Widely Used At The
Time Of Construction.

Module Split-ups
1.Image Pre Processing.
2.Identification
3.Feature Extraction
4.Weapon and militant Recognition
5.Militant Detection
6.Intimation

Image Pre-processing
Image Processing Is A Mechanism That Focuses On The Manipulation Of Images In Different Ways In Order
To Enhance The Image Quality. Images Are Taken As The Input And Output For Image Processing
Techniques. It Is The Analysis Of Image To Image Transformation Which Is Used For The Enhancement Of
Image

Identification
In This Stage Identify The Region Which Needs To Proceed For Further Process, It Is Involved In The
Identification Of The Particular Region Of The Image That Is Used For The Further Process Like Feature
Extraction And Classification Of The Images.
The Output Of The Pre-processing Step Is Given As The Input For The Identification Process. This Process Is
Based On The Binary Values Obtained In The Pre-processing Step. The Region With Black Is Considered A
Region Of Interest. The Region Of Interest Obtained By The Pre-processing Of The Images. That Region Is
Considered As Proceeding Part Of The Image From Which Weapon And Militant Will Be Identified. The
Identified Weapon And Militant Images Are Given To The Feature Extraction Process

Feature Extraction
In This Stage Extract The Required Feature From The Identified Region Which Is Obtained From The
Previous Step. That Region Is Compressed By Converting A Reduced Size Matrix To Control Over Fitting.
The Reduction Of The Matrix Size Helps In Reducing The Memory Size Of The Images. Then The Flattening
Process Is Applied To The Reduced Matrix, In Which The Reduced Matrix Is Converted To A One-dimension
Array, Which Is Used For Final Detection.

Weapon and militant Recognition
In This Stage One-dimension Array Is Used For The Final Classification Process. The Output Image
Obtained From Feature Extraction Is Given As Input To This Process. Where Continuous Classification Of
All The Features Obtained From The Previous Stage. Artificial Neural Networks Are Applied In This
Process.
Each Node Of The Input Layer Has A Value From A One Dimension Array Which Represents The Feature
From The Extracted Region.
That Is Sent To The Hidden Layer. Multiple Features Are Getting From The Input Layer And Undergo
Multiple Iteration In The Hidden Layer.
 Finally Get The Predictive Values By Applying SoftMax Activation Function To It. Finally, Get Some
Output Values From This Process And These Values Undergo Further Process.
The Highest Value In The Predictive Value Is Considered As Output Identified As Weapon And Militant. By
Using These Methods, The Weapon And Militant Will Be Detected By Considering Highest Accuracy
Values.

Intimation
◦ Telebooth Is A Bot Framework For Telegram Bot API. This Package Provides The Best Of Its Kind API For
Command Routing, Inline Query Requests And Keyboards, As Well As Callbacks.
◦ Bots Have Legitimate Uses On The Popular Chat Apps, But Are Being Exploited By
Cybercriminals. Cybercriminals Are Using Bots Deployed In Popular Messaging Apps Discord And
Telegram To Share Alerts, New Research Has Revealed.

Conclusion
The Proposed Project Aimed To Develop A Deep Learning Model That Can Detect Militants In Real-time
Using The YOLO Algorithm. The Project Was Implemented In Five Modules, Including Dataset Preparation,
Yolov3 Model Training, Real-time Detection, Performance Evaluation, And Voice Announcement.
The Project Has Potential Applications In The Security And Defense Sectors, Where The Detection Of
Militants Is Of Critical Importance. The Model Was Trained And Evaluated Using Precision, Recall, And F1
Score Metrics, And The Results Showed High Accuracy In Detecting Militants In Real-time.
The Voice Announcement System Was Integrated With The Real-time Detection Module, Which Provided An
Additional Layer Of Security And Alerted The Security Personnel Immediately. The Project Has The Potential
To Enhance The Security And Defense Sectors By Providing A Fast And Accurate Militant Detection System.

REFERENCES
Harsha Jain Et.Al. “Weapon And Militant Detection Using Artificial Intelligence And Deep
Learning For Security Applications” ICESC 2020.
Arif Warsi Et.Al “Automatic Handgun And Knife Detection Algorithms” IEEE Conference
2019.
Neelam Dwivedi Et.Al. “Weapon And Militant Classification Using Deep Convolutional
Neural Networks” IEEE Conference CICT 2020.
Kumar Verma Et.Al. “Handheld Gun Detection Using Faster RCNN Deep Learning” IEEE
Conference 2019.
Https://Www.Techtarget.Com/Searchenterpriseai/Definition/Convolutional-neuralnetwork
Https://Www.Researchgate.Net/
Https://Towardsdatascience.Com

army target detection using machine learning

army target detection using machine learning

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