AI Based Face mask detection using Yolvo7

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AI Based Face mask detection using Yolvo7

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1.Abstract

The outbreak of global pandemics, such as COVID-19, has highlighted the critical importance of preventive measures like wearing face masks to reduce the spread of infectious diseases. Monitoring mask compliance in public places manually is challenging, time-consuming, and often ineffective. To address this issue, this project proposes an AI-Based Face Mask Detection System using YOLOv7, which automates the process of detecting whether individuals are wearing face masks in real time.

The proposed system leverages advanced computer vision and deep learning techniques, specifically the YOLOv7 (You Only Look Once version 7) object detection model, to identify faces and classify them into two categories: with mask and without mask. A custom dataset was created and used for training the model, consisting of images of individuals wearing masks and not wearing masks. The dataset was preprocessed and annotated to improve model accuracy and performance.

The YOLOv7 model is trained on this dataset to achieve high-speed and accurate detection. Once trained, the model is capable of processing real-time video streams or uploaded video files. The system detects faces in each frame and classifies them accordingly, displaying bounding boxes with labels indicating mask status.

To enhance user interaction, a Tkinter-based graphical user interface (GUI) is implemented. The GUI allows users to upload video files or access live camera feeds, and it displays detection results in an intuitive and user-friendly manner. The system can also generate alerts or notifications when individuals without masks are detected, making it useful for monitoring public safety compliance.

This solution reduces the need for manual supervision, improves efficiency, and ensures better enforcement of health guidelines. Although the system performs effectively, challenges such as varying lighting conditions, occlusions, and different face orientations may affect accuracy. However, these limitations can be addressed with larger and more diverse datasets and improved model training.

In conclusion, this project demonstrates how artificial intelligence and computer vision can be effectively applied to public health monitoring. The proposed face mask detection system is scalable, cost-effective, and can be deployed in various environments such as hospitals, airports, schools, and public transport systems to ensure safety and compliance.

2. Objectives

To detect faces and identify mask usage in real time.
To classify individuals as “mask” or “no mask.”
To automate monitoring in public and private spaces.
To reduce manual supervision efforts.
To improve public health safety compliance.

3.Existing System

• Traditional monitoring systems rely on manual supervision to check whether individuals are wearing face masks.

• Security personnel are required to continuously observe CCTV footage, which is time-consuming and inefficient.

• Some systems use basic image processing techniques for face detection, but they lack accuracy in real-world conditions.

• These methods are not suitable for large-scale environments such as airports, malls, and public transport systems.

Limitations:

Time-consuming and labor-intensive.
Prone to human errors.
Difficult to monitor large crowds.
No real-time automated alerts.

4.Proposed System

• Develop an AI-Based Face Mask Detection System using deep learning techniques.

• Use YOLOv7 object detection model to detect faces and classify them as with mask or without mask.

• Train the model using a custom dataset containing masked and unmasked faces.

• Process real-time video or uploaded video using computer vision techniques.

• Implement a Tkinter GUI to display detection results in a user-friendly interface.

• Generate alerts when a person is detected without wearing a mask.

Key Features:

Real-time face and mask detection.
High accuracy using deep learning.
Automatic alert system for violations.
Works with images, videos, and live streams.
Scalable for large public environments.

5. Implementation Procedure

Module 1: Dataset Collection

Collect images of people with and without masks.
Label dataset (mask, no mask).

Module 2: Data Preprocessing

Clean and augment dataset.
Prepare annotations for YOLO training.

Module 3: Model Training

Train YOLOv7 model on custom dataset.
Optimize for accuracy and speed.

Module 4: Model Inference

Capture live video or input images.
Detect faces and classify mask usage.

Module 5: Alert System

Trigger alerts for “no mask” detection.
Log violations if required.

Module 6: Visualization

Display bounding boxes with labels.
Show real-time results on screen.

6.Software Requirements

Operating System : Windows 10 / Linux

Programming Language : Python

Libraries : OpenCV, NumPy, TensorFlow / PyTorch

Framework : Tkinter

Front End : GUI (Tkinter)

Database : Optional (MySQL / SQLite)

7.Hardware Requirements

Processor : Intel i3 or above

RAM : 4 GB (8 GB recommended)

Hard Disk : 500 GB

Camera : Webcam / CCTV

GPU (optional) : For faster model processing

8. Advantages of the Project

Real-time monitoring of mask compliance.
Reduces manual effort and human error.
Helps enforce public safety guidelines.
Scalable for crowded environments.
Cost-effective and easy to deploy.

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