Enhance road safety with real-time helmet detection powered by YOLOv8.

Helmet Detection Using Yolov8

## Overview

Helmet Detection Using Yolov8 is a dataset for object detection tasks - it contains Objects A8VE annotations for 611 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

01 Yolov8 Helmet Final Use No Augmentation

## Overview

01 Yolov8 Helmet Final Use No Augmentation is a dataset for object detection tasks - it contains Helmet TD7A PJRw S3mj annotations for 908 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Helmet Detection Using Yolo V8

## Overview

Helmet Detection Using Yolo V8 is a dataset for object detection tasks - it contains Motorcycle annotations for 214 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This dataset is designed for real-world object detection tasks, specifically focused on detecting hard hats (safety helmets) worn by individuals in construction and industrial environments. It was sourced and exported from Roboflow Universe, and comes with high-quality annotations in YOLOv5 format, making it ideal for training deep learning models for safety compliance and human detection.

Use Case

Hard hats are critical for personal safety on construction sites. Automating their detection can help:

• Enforce safety regulations
• Improve site monitoring
• Build smart surveillance systems
• Advance computer vision projects in industrial AI

Dataset Contents

The dataset includes:

• Images divided into train, valid, and test sets
• Bounding box annotations in YOLO format
• A data.yaml file for quick model training

Applications

• Object detection with YOLOv5, YOLOv8, Ultralytics
• Safety monitoring systems
• AI-powered construction site automation
• Computer vision model benchmarking

Credits

Originally published on Roboflow Universe, this dataset is shared for educational and research purposes.

Yolov8 Helmet Final Use

## Overview

Yolov8 Helmet Final Use is a dataset for object detection tasks - it contains Helmet TD7A annotations for 908 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Face With Helmet Detection

## Overview

Face With Helmet Detection is a dataset for object detection tasks - it contains Face With Helmet annotations for 935 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

In the context of construction site safety management, real-time object detection is crucial for ensuring workers’ safety through accurate detection of safety helmets. However, traditional object detection methods often face numerous challenges in complex construction environments, such as low light, occlusion, and the diverse shapes of helmets. To address these issues, we propose an improved helmet detection model, YOLOv8-CGS, which is based on the YOLOv8 architecture and integrates optimization modules such as CBAM (Convolutional Block Attention Module), GAM (Global Attention Mechanism), and SLOU (Smooth Labeling Loss Function). The goal is to enhance the model’s detection accuracy and robustness in complex scenarios. Specifically, GAM improves the model’s attention to key regions, CBAM enhances its ability to perceive important features, and SLOU optimizes the accuracy of bounding box predictions, particularly in complex and occluded environments. Experimental results show that YOLOv8-CGS achieves accuracy rates of 94.58% and 92.38% on the SHD and SHWD datasets, respectively, which represent improvements of 5.9% and 5.94% compared to YOLOv8. This enhancement allows YOLOv8-CGS to provide more efficient and accurate helmet detection in practical applications, significantly improving the real-time monitoring capabilities for construction site safety management.

The Safety Vests Detection Dataset is a curated collection of real‑world images annotated for detecting high‑visibility safety vests. Originally sourced from the Roboflow Universe “Safety Vests” project, this dataset aims to accelerate the development of computer‑vision systems for personal protective equipment (PPE) compliance, worker‑safety monitoring, and automated surveillance in industrial and construction environments.

• Origin & Purpose Collected and labeled by Roboflow contributors, this dataset provides a robust benchmark for object‑detection research focused exclusively on safety‑vest usage. It supports applications such as:

  • Automated site‑safety compliance checks
  • Real‑time PPE monitoring on video feeds
  • Integration with smart‑helmet or wearable‑tech systems universe.roboflow.com

• Dataset Composition

  • Total images: 3,897 high‑resolution photos featuring workers both with and without safety vests
  • Annotations: Bounding boxes around each person instance, labeled as:
  1. Safety Vest
  2. No Safety Vest
  3. Total annotations: ~4,200 boxes (two-class)
  4. Image environments: Indoor work sites, outdoor construction zones, varying lighting conditions, occlusions, and multiple viewpoints

• Annotation Format & Structure Exported in YOLO v5 format, the dataset follows this folder layout:

  /images/
   ├── train/    # 80% of images
   ├── valid/    # 10% of images
   └── test/     # 10% of images
  

  You can easily convert to COCO, Pascal VOC, TFRecord, or other common formats via Roboflow’s export tools.

• Recommended Splits

  • Training: ~3,118 images
  • Validation: ~389 images
  • Test: ~390 images (These splits were generated via random 80/10/10 sampling but can be adjusted to suit your experimental design.)

• Key Use Cases

  • Benchmarking new object‑detection architectures (YOLOv8, Faster‑RCNN, SSD, etc.)
  • Transfer learning for related PPE‑detection tasks (helmets, gloves, goggles)
  • Prototype development for edge‑deployable safety monitors

• Limitations & Considerations

  • Class imbalance: More “Safety Vest” instances than “No Safety Vest”—consider augmentation or re‑sampling if training from scratch.
  • Environmental bias: Majority of images depict daylight scenes; for low‑light performance, you may need to fine‑tune on dim‑light data.
  • Annotation consistency: Bounding‑box precision varies slightly—review for your precision requirements.

• License & Citation Shared under CC BY 4.0. When using or publishing results on this dataset, please cite:

  “Safety Vests Detection Dataset (v1.0), Roboflow Universe, 2025. Available at https://universe.roboflow.com/roboflow-universe-projects/safety-vests” And include the following in your acknowledgments: “Dataset originally sourced and annotated by the Roboflow community.”

Helmet-Wearing Detection

Using the YOLOv8 a state-of-the-art (SOTA) YOLO model to Detect > then control the microcontroller

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Personal Protective Equipment (PPE) Detection – 6 Classes Dataset

This dataset is designed for object detection of PPE compliance in workplace environments. It contains annotated images with six different categories of personal protective equipment and violations.

📁 Dataset Source - Provided by Roboflow - Version: Public, available for academic and commercial research - License: CC0 (Public Domain)

🔍 Classes (6): 1. Helmet
2. No-Helmet
3. Goggles
4. No-Goggles
5. Vest
6. Person

These categories help assess workplace safety and detect violations such as missing helmets or goggles.

📸 Image Details: - Format: JPG - Resolution: Varies - Annotations: YOLO format (.txt), with bounding boxes - Dataset split: Train / Valid / Test

✅ Use Case: This dataset is ideal for training YOLOv8 and other object detection models to develop real-time workplace safety systems, monitor PPE compliance, and enhance occupational safety measures.

🔧 Example Applications: - Construction site surveillance
- Real-time safety monitoring in manufacturing
- Smart factory worker detection and analytics

📌 Note: If you're using this dataset in your project or publication, make sure to credit Roboflow and cite the source appropriately.

Results of ablation studies on SHD and SHWD datasets.

YOLOV8 Hat Detection

## Overview

YOLOV8 Hat Detection is a dataset for object detection tasks - it contains Helmet annotations for 6,950 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Designed for industrial safety applications, this dataset provides high-quality, well-annotated data focusing on the detection of Personal Protective Equipment (PPE) and is particularly suitable for the training and application of computer vision models. The dataset contains 3,212 images of 640×640 pixels and focuses on the detection of PPE, such as the wearing of helmets and reflective undershirts. The data comes from a variety of sources, including public platforms such as GitHub, Kaggle, Roboflow, and Google Images, as well as real-life photographs from different scenarios, to ensure that the data is diverse and can be adapted to a variety of scenarios and applications. The dataset is labeled and categorized according to the official YOLO specification, and the data can be directly applied to mainstream object detection frameworks such as YOLOv8 and YOLOv11, making it an important resource for researchers, developers, and practitioners. This dataset can be used to improve industrial safety monitoring systems and enhance construction site safety.

Helmet De

## Overview

Helmet De is a dataset for object detection tasks - it contains Helmet annotations for 629 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Helmet Object

## Overview

Helmet Object is a dataset for object detection tasks - it contains Head Helmet Person annotations for 5,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

HelmetDetectionImageDataset

## Overview

HelmetDetectionImageDataset is a dataset for instance segmentation tasks - it contains Helmet annotations for 1,033 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

PPE Dataset for Workplace Safety

Overview

The "PPE Dataset" is a robust and diverse collection of images designed for the development and enhancement of machine learning models in the realm of workplace safety. This dataset focuses on the detection and classification of various types of personal protective equipment (PPE) typically used in industrial and construction environments. The goal is to facilitate automated monitoring systems that ensure adherence to safety protocols, thereby contributing to the prevention of workplace accidents and injuries.

Class Types

The dataset comprises annotated images spanning four primary PPE categories:

Boots: Safety footwear, including steel-toe and insulated boots. Helmet: Various types of safety helmets and hard hats. Person: Individuals, both with and without PPE, to enhance person detection alongside PPE recognition. Vest: High-visibility vests, reflective safety gear for visibility in low-light conditions. Ear-protection: adding images Mask: Respiratory masks adding images Glass: Safety glasses adding images Glove: Safety Gloves adding images Safety cones: to be added Each class is annotated to provide precise bounding boxes, ensuring high-quality data for model training.

Current Status and Timeline

Phase 1 - Collection: Gathering images from diverse sources, focusing on different environments, lighting conditions, and angles. Phase 2 - Annotation: Ongoing process of labeling the images with accurate bounding boxes. Phase 3 - Model Training: Scheduled to commence post-annotation, targeting advanced object detection models like YOLOv8 & YOLO-NAS.

Contribution and Labeling Guidelines We welcome contributions from the community! If you wish to contribute images or assist with annotations:

Image Contributions: Please ensure images are high-resolution and showcase clear instances of PPE usage. Annotation Guidelines: Follow the standard annotation format as per Roboflow's Annotation Guide. Your contributions will play a vital role in enhancing workplace safety through AI-driven solutions.

Riders Datasets

## Overview

Riders Datasets is a dataset for instance segmentation tasks - it contains Rider CNls annotations for 1,395 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

HELMETSANDBIBDETECTIONS

## Overview

HELMETSANDBIBDETECTIONS is a dataset for instance segmentation tasks - it contains HELMET_BIB annotations for 752 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.