Our Instructors Collaborate With Top Tech Leaders
Startups
Microsoft
NVIDIA
Create a safety compliance system that detects helmets, vests, gloves, and masks using object detection. Ensure workplace safety using AI vision.
Intermediate-level. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to land your first computer vision job or freelance clients.
Learn how AI ensures workplace safety by detecting personal protective equipment (PPE) like helmets, vests, and masks.
Use Python with OpenCV and YOLO or similar models to detect safety gear in real time.
Build a real-time monitoring system that checks for PPE compliance using surveillance footage.
Record PPE violations or compliance events with timestamps into a safety database for audits.
Design a visual dashboard to show live camera feed and PPE detection alerts clearly.
Finish the course and receive a verified certificate of success.
Muhammad Yaqoob is the founder of Tentosoft Pvt Ltd and a seasoned Computer Vision expert. With 10+ years of experience and over 5,000+ students taught globally, he brings deep industry knowledge and a passion for practical, hands-on learning.
8 weeks of comprehensive training with 50+ lessons and 10+ hours of content
PPE Detection System is a deep learning-based approach that automatically identifies whether individuals are wearing the required personal protective equipment (PPE) such as helmets, vests, and masks. It ensures workplace safety by analyzing visual inputs and providing real-time alerts to prevent safety violations.
Course Introduction and Features
Detects presence of helmets, vests, and masks using AI.
Supports automation of safety enforcement
Enhances overall safety and accountability
The environment setup involves installing Python and essential libraries, configuring IDEs like VS Code or Jupyter Notebook, and preparing the system for smooth development and execution of the PPE detection model.
Installing Python
VS Code Setup for Python Development
The PPE Detection Project focuses on building an AI-based solution that detects safety gear on workers in real-time. The overview includes the goals, dataset source, tools used, model architecture, and the overall workflow of the system.
PPE Detection System Project Overview
Builds an AI model to detect PPE like helmets, vests, and masks
Employs datasets annotated with PPE categories
Provides a scalable solution for industrial environments
Files such as datasets, model files, or notebooks are uploaded to Google Colab for cloud-based execution. This allows for efficient training and testing of the detection model using GPU acceleration.
File Uploaded on Google Colab
Uploads dataset files to Colab environment for access
Ensures model weights are available for training/inference
Keeps project organized within Colab workspace
Implementing accessibility testing protocols
Conducting A/B testing for design variations
Measuring and analyzing user engagement metrics
Dataset Visualization involves displaying sample images and class distributions to understand the dataset structure. This step helps verify labeling quality and provides insight into data diversity before training the model.
Dataset visualization
This section describes the deep learning model used for PPE detection, including the architecture (e.g., YOLOv7), input dimensions, number of output classes, and the training methodology adopted for accurate prediction.
PPE Model Information
PPE Code Execution covers the implementation and testing of the detection model. It runs the complete pipeline—loading the model, processing inputs, detecting PPE items, and displaying results with bounding boxes.
PPE Code Execution
This step involves opening and working on the project in Visual Studio Code. It includes editing scripts, debugging code, and managing project files in a structured development environment.
VS Code Open
All necessary Python packages and custom modules are imported in this phase. This typically includes libraries like TensorFlow, OpenCV, NumPy, and Matplotlib to support data processing, model handling, and visualization.
Packages and Flask Module Import
NVIDIA Nim is an AI toolset or interface from NVIDIA. This section outlines its role in enhancing performance for deep learning applications using GPU acceleration, ensuring faster model inference and training.
NVIDIA Nim Information
Is This Course Right for You?
Kickstart your AI journey with structured, hands-on learning.
Build a portfolio that recuriters can't ignore.
Add powerful AI/CV features to your apps and software.
Upskill for higher-paying, future-ready tech roles.
Build Smarter, more intelligent applications.
Transition into AI even with zero background.
One-time payment for lifetime access to all course materials and updates
Rorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam eu turpis molestie, dictum est a, mattis tellus. Sed dignissim, metus nec fringilla accumsan, risus sem sollicitudin lacus, sed risus a, mattis tellus. Sed dignissim, metus nec fringilla accumsan, risus sem sollicitudin lacus, sed risus .
Highly recommended for small teams who seek to upgrade their time & perform.
₹ 6720 inclusive of GST ₹ 13999
52% OFF🎁 Coupon Code:
Secure Payment Gateway
You can add this certificate in your Resume! Share it with your LinkedIn network 🚀
This Certificate Is Proudly Presented To
This certificate is given to Jacqueline Thompson for her achievement in the field of education and proves that she is competent in her field.
Signature
CERTIFICATE
Date of Event
Get full project code for 20+ real-world applications – build, customize, and learn hands-on with working solutions.
Join weekly live Q&As to resolve queries and deepen your understanding with real-time support
Enhance your confidence with communication tips, resume builder templates, and personal branding guides tailored for tech careers.
Get feedback, share wins, and grow with other learners in a safe and supportive environment.
Enroll today to claim all bonuses before the offer expires!
Get Instant Access
