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METHODOLOGY

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2.

YOLO V5

  • Data collection and preparation
  • Model selection
  • Model fine tuning
  • Training process
  • Post processing
  • Model testing
  • Real time inference
  • Deployment
  • Continuous monitoring and improvement

YOLO (You Only Look Once) is an object detection algorithm that can identify and locate multiple objects in an image or frame in a single forward pass through a convolutional neural network (CNN).

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PROBLEM STATEMENT

  • Creating an innovative system using YOLO v5 for real-time weapon detection to address the growing concern of public safety and security, enabling quicker and more accurate identification of potentially dangerous situations.

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Weapon Detection using YOLO V5

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OBJECTIVE

The "Detection" objective involves the system's ability to accurately locate and identify weapons in images or videos, providing real-time responses in security or law enforcement scenarios, while also maintaining reliability and adaptability under various conditions.

Presented by

Harivarsh Joshi (2VD20EC021)

Kartik M Pented (2VD20EC023)

Navinya J Aikal (2VD20EC033)

Noopur k Mahendrakar (2VD20EC038)

Weapon detection using YOLOv5 is a computer vision application that leverages the YOLO (You Only Look Once) deep learning architecture, specifically the YOLOv5 variant,

YOLOv5 is an improved and more efficient version of the YOLO series of object detection models, known for its real-time and accurate object detection capabilities.

  • Threat detection
  • Security enhancement
  • Privacy and ethical consideration
  • User education and acceptance

Introduction

RESULT AND ANALYSIS

  • High Accuracy: The system demonstrated a commendable level of accuracy in identifying weapons across various scenarios.

  • Real-time Processing: It successfully maintained real-time processing capabilities without compromising detection accuracy.

  • Low False Positives: The number of false positive detections was minimized, contributing to the system's reliability.

  • Ethical Considerations: The analysis addressed ethical concerns, ensuring fairness and privacy in weapon detection.
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