Spycrop
This web application utilizes computer vision to detect whether a person is wearing a mask. If the app detects the absence of a mask, it triggers an alert mechanism
2022
Role
Fullstack Developer
Timeline
March 2022 - May 2022
My Role
Fullstack Developer
Timeline
March 2022 - May 2022
Overview
Spycrop is a web application that utilizes computer vision to detect whether a person is wearing a mask. It uses machine learning models to analyze images and identify individuals without masks. If the application detects the absence of a mask, it triggers an alert mechanism, notifying the user and relevant authorities. SpyCrop is designed to be fast, accurate, and user-friendly, with a clean and intuitive interface.
Product Thinking
Problem & Solution
The Problem
The COVID-19 pandemic has highlighted the importance of wearing masks in public spaces. However, monitoring mask compliance can be challenging, especially in high-traffic areas like airports, hospitals, and schools.
The Solution
Spycrop uses computer vision and machine learning to detect individuals without masks, helping organizations monitor mask compliance and enforce safety protocols. By providing real-time alerts and notifications, the application helps prevent the spread of COVID-19 and other infectious diseases. It also provides attendance tracking based on facial recognition to track attendance and mask compliance for employees, students, and visitors.
Technical Foundation
Technologies
01
Python
02
Flask
03
OpenCV
04
TensorFlow
05
HTML
06
CSS
07
JavaScript
Product Capabilities
Highlights
Mask Detection: Identify individuals without masks using computer vision and machine learning models.
Alert Mechanism: Trigger alerts and notifications when the application detects the absence of a mask.
Real-Time Monitoring: Monitor mask compliance in real time and generate reports for analysis.
Attendance Tracking: Track attendance and mask compliance for employees, students, and visitors by facial recognition.
Project Links
