Safety_Helmet_Detection

1. Project Title

This project uses YOLOv10 for object detection to determine whether construction workers are wearing safety helmets on site.

Click here to download the dataset

Click here to have more details about the dataset

Click here to download the model's weight

2. Project Description

This project aims to enhance safety on construction sites by using state-of-the-art object detection techniques. We employ the YOLOv10 model to determine whether construction workers are wearing safety helmets.

Key Features

• Image-Based Detection: The current system processes images to detect the presence of safety helmets.
• High-Accuracy: By leveraging the YOLOv10 model, the project ensures precise detection, minimizing false positives and negatives.

Technology Stack:

• YOLOv10:
• Pytorch:
• OpenCV

This project is designed to provide a simple yet practical and efficient solution to enhance safety measures on construction sites by leveraging the latest advancements in computer vision and machine learning.

3. Folder Structure and Installation

Safety_Helmet_Detection/
├── .gitignore
├── app.py
├── README.md
├── requirements.txt
├── sonar-project.properties
├── tmp.py
├── .github/
│   ├── workflows/
│   │   ├── build.yml
├── models/ 
│   ├── best.pt
├── src/
│   ├── detect.py

• Download the model's weights, create a folder in the Safety_Helmet_Detection repo, name it models, and put the weight in that folder

3. How to Install and Run the Project

1. Git clone this project

git clone https://github.com/TinhAnhGitHub/Safety_Helmet_Detection.git

2. Create a virtual environment, and activate it

python -m venv venv
.\venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run the streamlit application, don't forget to create the models and download the weight

streamlit run app.py