International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
MILITANT INTRUSION DETECTION USING MACHINE LEARNING Ramyasri. S1, Kavya. S2, Sowbhagya. S3, Prerna4, Dr. S. G. Hiremath5 1,2,3,4Student, Department of Electronics and Communication Engineering, East West Institute of Technology,
Bangalore, India
5H.O.D, Department of Electronics and Communication Engineering, East West Institute of Technology,
Bangalore, India ---------------------------------------------------------------------***--------------------------------------------------------------------1.1 Objectives Abstract - The project is being used for monitoring, and live-tracking. The prototype is used in livesurveillance for monitoring and detecting abnormal events based on real-time image processing techniques. Operations of this project have three processing modules, the first processing module is for object detection using the YOLO-V5 algorithm and the second processing module is for monitoring and alarm operations will be carried out by the third processing module.)
Key Words: Live-tracking, Live-surveillance, Object detection, Ultrasonic sensor, YOLO-V5 algorithm.
It detects guns, grenades, and tankers.
Once the system detects the objects the detection details are stored & it will send alerts to the admin side (control room).
1.3 Motivation Nowadays, the protection of borders and personnel areas becomes very important. Video surveillance plays an important role in real-time. Due to these requirements, cameras are installed at every corner and the video surveillance, system detects the scene and automatically detects abnormal activities and entrances.
1.4 Existing System The existing system does not distinguish between normal and abnormal events, resulting in police arriving at crime scenes less and less frequently unless there is visual verification, either by manned patrols or by electronic images from surveillance cameras [12]. Irregularity or anomaly detection is the identification of irregular, unexpected, unpredictable, unusual events or elements that are not considered normally occurring events or regular elements in a pattern or element in a data set and thus differ from existing patterns [6][10]. An anomaly is a pattern that occurs deviantly from a set of standard patterns [14].
The YOLO-V5 (You Only Look Once) is a neural networkbased algorithm specifically used to classify objects such as weapons, fire, and water drops. It is popular because of its speed and accuracy. The algorithm creates a box for each object and detects parts of the object. When it detects something related to the input, the object in question is detected [16]. A key technique used in YOLO models is non-maximal maximum suppression (NMS). NMS is a post-processing step used to improve the accuracy and efficiency of object detection. Object detection typically creates multiple bounding boxes for a single object in an image. These bounding boxes may overlap or be in different positions, but they all represent the same object.
Impact Factor value: 8.226
Develop a military robot that can perform complex tasks in difficult and dangerous environments, such as battlefield surveillance and threat neutralization.
The Militant Intrusion Detection System is very important for the military. This system detects weapons, grenades, armored vehicles, land mines, and intruders. The real goal of this system is to increase the accuracy of detection of weapons and intruders. The system works based on the YOLO-V5 algorithm, a subtopic of machine learning. The detection robot consists of a Raspberry Pi that contains the detection program. Once the robot detects a weapon or intruder, it sends a message displayed on the LCD screen. Landmine detection is an additional function of the robot, which is performed with a metal sensor. By using this robot, many attacks can be detected in advance [1], [3], [15].
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This prototype implements the detection of different warship objects using a YOLO(You Only Look Once) algorithm which is the base of CNN layers(Convolutional Neural network).
1.2 Problem statement
1. INTRODUCTION
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2. LITERATURE SURVEY I Peng Zhao and Lingren Kong used the YOLO-V3 algorithm, which was slow and less accurate [5], [9].
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