jhboyo/ppe-dataset

PPE Detection Dataset (3-Class)

딥러닝 기반 건설현장 안전 장비(PPE) 착용 모니터링을 위한 데이터셋

Dataset Description

개인보호구(Personal Protective Equipment) 착용/미착용 상태를 감지하기 위한 YOLO 형식의 객체 탐지 데이터셋입니다.

주요 특징:

• ✅ 헬멧 착용 감지 (helmet)
• ⚠️ 헬멧 미착용 감지 (head) - 실시간 안전 경고 가능
• ✅ 안전조끼 착용 감지 (vest)
• 15,500개 이미지, 60,991개 객체
• YOLOv8 최적화 포맷

Classes

Class ID	Class Name	Description
0	helmet	안전 헬멧 착용 ✅
1	head	헬멧 미착용 (머리만) ⚠️
2	vest	반사 안전 조끼 착용 ✅

Dataset Statistics

Split	Images	Labels	Helmet	Head	Vest	Total Objects
Train	9,999	9,999	25,425	3,679	10,351	39,455
Val	2,750	2,750	6,793	1,144	2,737	10,674
Test	2,751	2,751	6,939	962	2,961	10,862
Total	15,500	15,500	39,157	5,785	16,049	60,991

Class Distribution:

• Helmet: 39,157개 (64.2%) - 헬멧 착용
• Head: 5,785개 (9.5%) - 헬멧 미착용
• Vest: 16,049개 (26.3%) - 안전조끼 착용

Split Ratio:

• Train: 64.5% (9,999 images)
• Val: 17.7% (2,750 images)
• Test: 17.7% (2,751 images)

Data Format

YOLO 형식 (normalized coordinates):

class_id x_center y_center width height

Example:

0 0.456789 0.345678 0.123456 0.234567  # helmet
1 0.234567 0.123456 0.098765 0.187654  # head
2 0.567890 0.456789 0.145678 0.256789  # vest

Dataset Structure

ppe-dataset/
├── train/
│   ├── images/     # 9,999 images
│   └── labels/     # 9,999 label files (3 classes)
├── val/
│   ├── images/     # 2,750 images
│   └── labels/     # 2,750 label files (3 classes)
└── test/
    ├── images/     # 2,751 images
    └── labels/     # 2,751 label files (3 classes)

Usage

Download with Hugging Face CLI

# Install huggingface-hub
pip install huggingface-hub

# Download dataset
huggingface-cli download jhboyo/ppe-dataset --repo-type dataset --local-dir ./dataset

Using with uv

uv tool install huggingface-hub
uv tool run hf download jhboyo/ppe-dataset --repo-type dataset --local-dir ./dataset/data

YOLO Training Configuration

Create a YAML configuration file:

# ppe_dataset.yaml
path: /path/to/dataset
train: train/images
val: val/images
test: test/images

nc: 3
names:
  0: helmet
  1: head
  2: vest

Training with YOLOv8

from ultralytics import YOLO

# Load model
model = YOLO('yolov8n.pt')

# Train
model.train(
    data='ppe_dataset.yaml',
    epochs=100,
    imgsz=640,
    batch=16
)

Data Sources

This dataset is merged from two Kaggle datasets:

1. Hard Hat Detection (5,000 images)

   • Original classes: helmet, head, person
   • Used: helmet, head (착용/미착용 모두 탐지)

2. Safety Helmet and Reflective Jacket (10,500 images)

   • Classes: Safety-Helmet, Reflective-Jacket
   • Used: both classes (helmet, vest)

Preprocessing

• VOC to YOLO format conversion for Dataset 1
• 3-Class Mapping:
  • helmet: 0 (헬멧 착용)
  • head: 1 (헬멧 미착용, Dataset 1 only)
  • vest: 2 (안전조끼 착용)
• File naming with prefix (ds1_, ds2_) to avoid conflicts
• Dataset split:
  • Train: 64.5% (9,999 images)
  • Val: 17.7% (2,750 images)
  • Test: 17.7% (2,751 images)
  • Seed: 42 (reproducible)

License

MIT License

Citation

@dataset{ppe_detection_2024,
  title={PPE Detection Dataset for Construction Safety},
  author={SafetyVisionAI Team},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/jhboyo/ppe-dataset}
}

Project

This dataset is part of the Safety Vision AI project - a deep learning-based construction site safety equipment monitoring platform.

Original Dataset Sources

This dataset is created by merging and preprocessing the following Kaggle datasets:

1. Hard Hat Detection Dataset

   • Source: Hard Hat Detection on Kaggle
   • Original classes: helmet, head, person
   • Format: Pascal VOC
   • Images: 5,000

2. Safety Helmet and Reflective Jacket Dataset

   • Source: Construction Site Safety Image Dataset on Kaggle
   • Original classes: Safety-Helmet, Reflective-Jacket
   • Format: YOLO
   • Images: 10,500

Acknowledgments: We thank the original dataset creators for making their work publicly available.