Weapon Detection System Using CNN FLask Web app

ML powered system for detecting weapons within images
Business Problem
Mass shootings
have become increasingly prevalent at public gatherings
Creating an algorithm that that be integrated into traditional surveillance systems can be used to detect threats faster and more efficiently than those monitored by people
In modern surveillance systems, there is a person or group of people, in charge of watching monitors which can span across multiple floors of a given area
Violence on social media platforms
such as Youtube, Facebook, and TikTok
An algorithm that integrate itself into traditional upload systems can detect violent videos before they are spread on a given website
Considering the graphs below, the United States ranks among the top 5 countries in terms of firearm deaths
Total Deaths
Data
Mass Shootings
Data
Solution
Create a neural network that can be integrated into traditional surveillance systems
This neural network will be able to detect whether a firearm is present in a frame, and if so, it will notify authorities/managers of its detection
Requirements
keras
(PlaidML backend --> GPU: RX 580 8GB)
numpy
pandas
opencv
(opencv-contrib-python)
matplotlib
beautifulsoup
Datasets
Data used in this project can be found on my Google Drive
Weapons
No Weapons

Detailed Project Description

Weapon Detection System Using CNN FLask Web app gives the user a focused everyday tool with a clean interface, local storage of their data and optional sync or reminders.

How It Works
1. The user enters or records data (amounts, steps, notes, recordings, values).
2. The app validates and stores it locally (SQLite/Room/SharedPreferences) or in the cloud.
3. Lists, totals, charts or conversions are computed and displayed instantly.
4. Reminders/notifications run in the background where the feature applies.

Technology Roles
- BLE
- Python
- Android
- Flask
- OpenCV
- Machine Learning
- Deep Learning

Implementation Steps
1. Sketch the screens and the data each screen needs.
2. Create the project in Arduino IDE and set up navigation between screens.
3. Define the local data model (SQLite/Room/SharedPreferences) or cloud sync.
4. Implement the core feature (tracking, conversion, recording, reminders, playback).
5. Add background work / notifications / permissions where required.
6. Test on real devices, handle edge cases and optimise battery and performance.
7. Generate the APK and prepare screenshots and documentation.

Key Features
- Simple, fast UI
- Offline storage
- Summaries and charts
- Notifications / sync where applicable

Expected Output
On completion the student will have an installable app that keeps the user's data organised and accessible.

Deliverables
- Complete source code with comments
- Database schema / data model and screen flow
- Project report with screenshots and results
- Demonstration-ready build