https://github.com/ZijianWang-ZW/PPE_detection

Real-time PPE detection based on YOLO. Open high-quality dataset.
https://github.com/ZijianWang-ZW/PPE_detection

construction-safety construction-site deep-learning ppe-detection yolo

Last synced: about 1 month ago
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Real-time PPE detection based on YOLO. Open high-quality dataset.

• Host: GitHub
• URL: https://github.com/ZijianWang-ZW/PPE_detection
• Owner: ZijianWang-ZW
• Created: 2020-08-10T03:27:59.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-11-03T08:35:47.000Z (about 2 years ago)
• Last Synced: 2024-10-05T16:05:46.047Z (3 months ago)
• Topics: construction-safety, construction-site, deep-learning, ppe-detection, yolo
• Homepage: https://github.com/ZijianWang1995/PPE_detection
• Size: 7.47 MB
• Stars: 47
• Watchers: 2
• Forks: 9
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# Real-time PPE Detection & Open Dataset ([Open Access paper link](https://www.mdpi.com/1424-8220/21/10/3478))

![avatar](figures/ppe_sample.gif)

## Introduction

The repository introduces eight DL models built on You Look Only Once (YOLO) architecture for PPE detection. Meanwhile, a novel high-quality dataset is constructed for detecting the person, the vest, and four helmet colors.

## Framework

![avatar](figures/methodology.PNG)

## CHV Dataset

A novel dataset  is constructed for detecting the helmet, the helmet colors and the person for this project, named **Color Helmet and Vest (CHV)** dataset. 

Instead of just accepting exiting images, strict criteria are designed at the beginning, and only  1,330 high-quality images among 10,000 ones from the Internet and open datasets are selected. 



The dataset is open for free use, please download at [Google Drive](https://drive.google.com/file/d/1fdGn67W0B7ShpBDbbQpUF0ScPQa4DR0a/view?usp=sharing) or [Baidu Yunpan (password: f003)](https://pan.baidu.com/s/1G9EbLKUgF1tcOPCeWSEeMw ). 

If the dataset helpes you, please cite the repository in your article:

`

@Article{wang2021ppe,

AUTHOR = {Wang, Zijian and Wu, Yimin and Yang, Lichao and Thirunavukarasu, Arjun and Evison, Colin and Zhao, Yifan},

TITLE = {Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches},

JOURNAL = {Sensors},

VOLUME = {21},

YEAR = {2021},

NUMBER = {10},

ARTICLE-NUMBER = {3478},

URL = {https://www.mdpi.com/1424-8220/21/10/3478},

ISSN = {1424-8220},

DOI = {10.3390/s21103478}

}`

Or

	

`Wang, Z.; Wu, Y.; Yang, L.; Thirunavukarasu, A.; Evison, C.; Zhao, Y. Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches. Sensors 2021, 21, 3478. https://doi.org/10.3390/s21103478`

## Results

- **YOLO v5x owns the best mAP, 86.55%.**

- **YOLO v5s has the faster processing speed, 52 FPS.**

- For YOLO v3 models, different detection layers are tested, while the more layers cannot improve the performance. 

- For YOLO v4 models,  the increase of training image size cannot contribute to better performance. 

 

Figure: Mean average precision in each model.




Figure: Average time for processing one image in each model (GPU: Tesla P40 with 24 GB; CPU: 4 cores with 8 GB). 


## 

If you are interested in my work, please visit