helmet_detection
This project aims to detect helmets in images using the YOLO object detection algorithm with CNN
Science Score: 44.0%
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Low similarity (8.7%) to scientific vocabulary
Repository
This project aims to detect helmets in images using the YOLO object detection algorithm with CNN
Basic Info
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- Watchers: 1
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Metadata Files
README.md
Helmet Detection using YOLO with CNN Architectures
This project aims to detect helmets in images using the YOLO (You Only Look Once) object detection algorithm with CNN (Convolutional Neural Network) architectures. The goal is to improve safety by identifying whether people in images are wearing helmets or not.
Overview
The YOLO algorithm is a popular choice for object detection tasks due to its speed and accuracy. By training YOLO on a dataset of images containing people, we can create a model that can detect the presence or absence of helmets on people's heads.
Dataset
The dataset for this project should include images of people wearing and not wearing helmets. Each image should be labeled to indicate the presence or absence of a helmet. The dataset should be divided into training, validation, and test sets for training and evaluating the model.
Model Architecture
The CNN architecture used in this project should be suitable for object detection tasks. You can experiment with different architectures such as ResNet, MobileNet, or custom architectures to achieve the desired performance.
Training
To train the model, you can use a deep learning framework such as TensorFlow or PyTorch. You will need to define the YOLO architecture, load the dataset, and train the model using the labeled images.
Evaluation
After training the model, you can evaluate its performance using the test set. You can calculate metrics such as precision, recall, and F1 score to measure the model's accuracy in detecting helmets.
Usage
- Dataset Preparation: Prepare a dataset of images containing people wearing and not wearing helmets, and label the images accordingly.
- Model Training: Train the YOLO model using the labeled dataset and a suitable CNN architecture.
- Model Evaluation: Evaluate the trained model using a test set to measure its performance.
- Deployment: Deploy the trained model for real-time helmet detection in images or videos.
Feel free to customize this README file to include any additional information or details specific to your project. Results:
https://github.com/Rahulraonimbalkar/helmet_detection/assets/117708809/de140834-6352-44ab-ab4e-69ea02e94b6c
Owner
- Name: Rahul nimbalkar
- Login: Rahulraonimbalkar
- Kind: user
- Location: USA
- Repositories: 1
- Profile: https://github.com/Rahulraonimbalkar
Software Engineer: React |App developer | Blockchain | AI & ML | Data analysis. Currently pursuing MS in Computer Science at SUNY Buffalo
Citation (CITATION.cff)
cff-version: 1.2.0
preferred-citation:
type: software
message: If you use YOLOv5, please cite it as below.
authors:
- family-names: Jocher
given-names: Glenn
orcid: "https://orcid.org/0000-0001-5950-6979"
title: "YOLOv5 by Ultralytics"
version: 7.0
doi: 10.5281/zenodo.3908559
date-released: 2020-5-29
license: AGPL-3.0
url: "https://github.com/ultralytics/yolov5"