Building a PPE Detection App with Python and AWS Rekognition

In industrial and construction environments, ensuring workers wear Personal Protective Equipment (PPE) is crucial for safety and compliance. Manual inspections are time-consuming and often inconsistent. What if you could upload a photo or video and get an instant PPE compliance report? With AWS Rekognition and Python, you can.

Whether you’re a student working on an AI-integrated web project or a developer exploring real-time safety systems, this project gives you a hands-on opportunity to build a real-world computer vision solution.

The Problem: Manual PPE Checks are Inefficient

Traditional methods of PPE compliance checks pose several challenges:

• Time-consuming and inconsistent

• Requires human supervision

• Difficult to scale for large operations

• Misses real-time alerts or analytics

  
  

The Solution: Automating PPE Detection with AWS Rekognition

AWS Rekognition provides a pre-trained PPE detection model that can identify protective equipment like helmets, face masks, and gloves in images and videos. When integrated with a Django-based web application, it forms a powerful tool for analyzing and reporting compliance.

Step-by-Step Guide to Building Your PPE Detection App

Step 1: Configure AWS Rekognition and S3

• Sign up or log into AWS

• Create an IAM user with permissions for Rekognition and S3

• Set up S3 for storing temporary media files

• Store your AWS credentials securely in Django settings

  
  

Step 2: Set Up the Django App

• Create a Django project and app

• Install necessary packages: pip install django boto3 pillow opencv-python

• Configure media storage and AWS settings.

  
  

Step 3: Build the Models and Forms

• Create a model to store media files and detection metadata

• Implement a Django form for handling file validation and upload

  
  

Step 4: Implement AWS Rekognition Integration

• Use an API to analyze uploaded media

• Support both S3 and direct image byte inputs

• Adjust detection settings like confidence threshold and strict compliance mode

  
  

Step 5: Add Image and Video Processing Logic

• For images, annotate detected persons and PPE with bounding boxes

• For videos, extract frames and process them sequentially

• Save processed results and detection data to CSV

  
  

Step 6: Build the Web Interface

• Drag-and-drop upload interface

• Real-time preview of uploaded media

• Settings panel to customize detection parameters

• Responsive result visualization with downloadable processed files and CSV

Why This Project Matters

By building this project, you demonstrate your ability to integrate AI services into real applications. It shows proficiency in:

• Cloud AI tools (AWS Rekognition)

• Image and video processing

• Web development with Django

• Real-world compliance and safety applications

Real-World Applications

• Construction safety systems

• Factory or industrial compliance audits

• Smart surveillance and monitoring systems

• Safety check apps for field engineers

Technical Features

• Multi-person detection and compliance summary

• Customizable PPE requirements and confidence levels

• Support for both image and video formats

• Exportable CSV reports with detection metadata

Need Help Building Your Project?

At CodersArts, we specialize in helping students build real-world, AI-powered solutions for assignments and academic projects. Whether you’re stuck on AWS setup, parsing data, or building the interface, we’re here to help you succeed.

You can also check out the project demo in the following video:

Need personalized guidance on this project or a similar one? Reach out to CodersArts today and get expert support tailored to your needs.  Visit www.codersarts.com or contact us at contact@codersarts.com.