Weapon detection with audio alert using efficientdet

Project Code :TCMAPY1600

Objective

The primary objective of this project is to develop an automated, real-time weapon detection system that identifies the presence of weapons and triggers an audio alert to warn authorities or nearby personnel. The project uses the EfficientDet deep learning model trained on a custom dataset containing images of various weapons and people

Abstract

In recent years, the rising incidence of violence in public spaces has underscored the urgent need for intelligent surveillance systems. This project proposes a real-time weapon detection system that leverages the EfficientDet object detection model to identify potential threats from camera feeds, including weapons such as Heavy Gun, Knife, Pistol, and sharp objects like "pisau". The system is capable of accurately detecting both individuals (Person) and the presence of weapons using a custom-trained EfficientDet model on a labeled dataset. Upon detecting a weapon, the system immediately triggers an audio alert, allowing timely human intervention. The frontend is designed using HTML and CSS, providing a user-friendly interface for real-time monitoring and alert notifications. The integration of EfficientDet ensures a balance between speed and accuracy, making the solution suitable for deployment in schools, malls, public transport hubs, and other high-risk environments. This project demonstrates the potential of combining deep learning-based object detection with real-time alert mechanisms to enhance public safety and proactive threat response. Keywords EfficientDet, Weapon Detection, Object Detection, Audio Alert System, Real-time Surveillance, Public Safety, Deep Learning, Security AI, HTML, CSS, Person Recognition, Knife, Gun, Pistol Detection.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Hardware Requirements

Component

Specification

Processor

Intel Core i5 or higher

RAM

Minimum 8 GB

Storage

Minimum 256 GB SSD

GPU (Optional)

NVIDIA GTX 1050 or above (for training)

Microphone/Speaker

For audio alert functionality

Camera (Optional)

Webcam or IP camera (for live detection)

 

Software Requirements

Software

Details

OS

Windows 10 / Ubuntu 20.04 or above

Programming Lang.

Python 3.7+

Libraries

TensorFlow, OpenCV, NumPy, Matplotlib

Frontend

HTML, CSS, JavaScript

IDE

VS Code / Jupyter Notebook

Web Framework

Flask or Django (for backend integration)

Browser

Google Chrome / Firefox

Demo Video