AI Based PPE Detection

-20%

AI Based PPE Detection

0 Orders 0 Wish listed

₹4,999.00

Qty

Total price:

₹4,999.00

Overview
Reviews

Detail Description

1.Abstract

Workplace safety is a critical concern across various industries such as construction, manufacturing, healthcare, and food processing, where adherence to Personal Protective Equipment (PPE) regulations is mandatory. PPE, including items such as helmets, gloves, face masks, safety shoes, and protective clothing, plays a vital role in minimizing occupational hazards and ensuring employee well-being. However, traditional methods of monitoring PPE compliance rely heavily on manual supervision by safety officers, which is often time-consuming, error-prone, and inefficient in large-scale environments.

This project presents an AI-based PPE detection system designed to automate the process of monitoring safety compliance in real time using computer vision techniques. The system utilizes surveillance cameras to capture live video feeds and applies deep learning algorithms to detect whether individuals are wearing the required PPE. A custom object detection model is developed using the YOLOv5 (You Only Look Once version 5) architecture, trained on a diverse dataset containing multiple PPE categories across different industrial scenarios.

The trained model performs real-time inference to identify the presence or absence of essential safety equipment such as helmets, gloves, face masks, and safety vests. If any violation of PPE compliance is detected, the system generates instant alerts to notify supervisors, thereby enabling prompt corrective action. Additionally, the system is integrated with a Flask-based web interface that allows users to monitor live detections and review compliance reports. A SQL database is used to store detection logs, including timestamps, detected objects, and violation records, facilitating future audits and analysis.

The proposed solution is scalable, efficient, and adaptable to various industrial environments with different PPE requirements. By reducing reliance on manual monitoring and improving detection accuracy, the system enhances workplace safety, ensures regulatory compliance, and minimizes the risk of accidents. Overall, this project demonstrates the practical application of artificial intelligence and computer vision in building intelligent safety monitoring systems for modern industries.

2. Objectives

To detect PPE compliance in real time using AI.
To identify safety gear such as helmets, vests, gloves, and masks.
To reduce workplace accidents by enforcing safety rules.
To automate monitoring in industrial environments.
To generate alerts for safety violations.

3.Existing System

• In traditional workplace safety monitoring, PPE compliance is ensured through manual supervision by safety officers and managers.

• Human monitoring is used to check whether workers are wearing safety equipment such as helmets, gloves, masks, and safety vests.

• Some basic surveillance systems are used, but they do not include intelligent detection or automated alert mechanisms.

• Existing systems rely heavily on human observation, which can lead to errors, especially in large-scale industrial environments.

Limitations:

Time-consuming and labor-intensive.
Prone to human error.
Difficult to monitor large areas continuously.
Delayed response to safety violations.

4.Proposed System

• Develop an AI-based PPE detection system using computer vision techniques.

• Implement deep learning models such as YOLOv5 for real-time object detection.

• Detect safety equipment like helmets, gloves, face masks, and safety vests from live video streams.

• Integrate the system with surveillance cameras for continuous monitoring.

• Generate real-time alerts when PPE violations are detected.

• Store detection results and logs in a database for future analysis and auditing.

• Provide a web-based interface using Flask for monitoring and visualization.

Key Features:

Real-time detection of PPE usage.
Automatic identification of violations.
Alerts for non-compliance.
Scalable for large industrial setups.
Integration with existing CCTV systems.

5. Implementation Procedure

Module 1: Dataset Collection

Collect images of workers with and without PPE.
Label data (helmet, vest, no-helmet, etc.).

Module 2: Data Preprocessing

Clean and augment dataset.
Prepare annotations for training.

Module 3: Model Training

Train YOLO model on PPE dataset.
Optimize for accuracy and speed.

Module 4: Model Inference

Capture live video stream.
Detect PPE in real time.

Module 5: Violation Detection

Check if required PPE is missing.
Identify non-compliance cases.

Module 6: Alert System

Trigger alarms or notifications.
Log violations for reporting.

6.Software Requirements

Operating System : Windows 10 / Linux

Programming Language : Python

Front End : HTML, CSS

Back End : MySQL / SQLite

Framework : Flask

Libraries : OpenCV, YOLOv5, TensorFlow / PyTorch

7.Hardware Requirements

Processor : Intel i3 or above

RAM : 4 GB (8 GB recommended)

Hard Disk : 500 GB

GPU (Optional) : For faster model training and inference

8. Advantages of the Project

Improves workplace safety compliance.
Real-time monitoring and alerts.
Reduces human supervision effort.
Prevents accidents and injuries.
Scalable for multiple sites.
Cost-effective and efficient solution.

No review given yet!

Fast Delivery all across the country

Safe Payment

7 Days Return Policy

100% Authentic Products

Shopping cart