This is a dataset of people wearing helmets. We collected 875 photos by ourselves, taken at the university gate and dormitory. The dataset consists of 2 classes: - helmet: includes the image of a person wearing a helmet, with different types of helmets: full face hats, 3/4 hats,... - no_helmet: includes images of people who don't wear helmets, and people with coats or hats.

HelmetViolations (2024-12-08)

This dataset, HelmetViolations, focuses on identifying and classifying motorcycle riders based on helmet usage and detecting motorcycle license plates from a top-view perspective. Exported via Roboflow on December 8, 2024, this dataset is designed for YOLOv9-based object detection tasks. It is particularly valuable for projects aimed at improving road safety and enforcing helmet laws through automated systems.

Dataset Overview

• Total Images: 1,004 (including augmented versions)
• Annotations: YOLOv9 format with three classes:
  • Plate
  • WithHelmet
  • WithoutHelmet
• Pre-processing:
  • Images resized to 640x640 resolution.
  • Auto-orientation applied (with EXIF stripping).
  • Grayscale conversion (CRT phosphor effect).

Augmentation Details

To enhance diversity and improve model generalization, the following augmentations were applied to create 3 versions of each source image:
- 50% probability of horizontal flip
- Random rotation between -15° and +15°

Data Splits

• Training Set: 363 images (original + augmented).
• Validation Set: 53 images.
• Test Set: Included in the export for model evaluation.

Use Cases

This dataset is ideal for:
- Helmet compliance monitoring systems.
- License plate detection and recognition tasks.
- General object detection research focusing on motorcycle-related scenarios.

Roboflow Integration

This dataset was created and managed using Roboflow, an end-to-end computer vision platform for dataset annotation, augmentation, and export.

Helmet Detection

## Overview

Helmet Detection is a dataset for object detection tasks - it contains Helmet Person Motorcycle annotations for 4,169 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.

Helmet Object Detection Dataset

The dataset consist of photographs of construction workers during the work. The dataset provides helmet detection using bounding boxes, and addresses public safety tasks such as providing compliance with safety regulations, authomizing the processes of identification of rules violations and reducing accidents during the construction work.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fc7a46d2407e8aa245f107524fcaecff5%2Fhelmets.png?generation=1686295342860797&alt=media" alt="">

Dataset structure

• img - contains of original images of construction workers
• boxes - includes bounding box labeling for the original images
• annotations.xml - contains coordinates of the bounding boxes and labels (helmet, no_helmet), created for the original photo

Data Format

Each image from img folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the bounding boxes and labels for helmet detection. For each point, the x and y coordinates are provided.

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fce2115cd583ab7bc4e1d3d2749b4d7ad%2Fcarbon%20(7).png?generation=1686295970420156&alt=media" alt="">

Helmet Detection might be made in accordance with your requirements.

🧩 This is just an example of the data. Leave a request here to learn more

🚀 You can learn more about our high-quality unique datasets here

keywords: object detection dataset, helmet detection, helmet recognition, helmet segmentation, hard hat dataset, construction dataset, construction industry dataset, construction data, manufacturing dataset, safety dataset, industrial safety database, health and safety dataset, quality control dataset, image dataset, quality assurance dataset

Bike Riders Helmet Detection

## Overview

Bike Riders Helmet Detection is a dataset for object detection tasks - it contains Helmet annotations for 506 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).

Security Helmet Detection

## Overview

Security Helmet Detection is a dataset for object detection tasks - it contains Security Helmet annotations for 5,112 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).

This dataset has nearly 28k labelled images, collected from different sources. The images have been preprocessed and split into Training, Validation and Test sets. The classes in this images are : 0 - 'Helmet', 1 - 'No Helmet', 2 - 'Worker'

This dataset contains 58,255 images from construction site scenes, include indoor and outdoor scenes. The data includes workers of Asian background. The data includes multiple devices, multiple lighting conditions, multiple scenes and multiple collection time periods. Annotations cover rectangular bounding boxes of human body, safety helmets and safety vests.It is suitable for construction site safety monitoring, PPE detection, and worker behavior analysis.

We extended the number of labels in Kaggle’s safety helmet detection dataset, which has 5000 images and 5000 annotations. The original dataset had three classes (person, head and helmet) and a total of 2501 labels. Moreover, the original dataset was incompletely labelled. We added three new labels on the dataset in results, the new labels consists of six classes (helmet, head with helmet, person with helmet, head, person no helmet, and face) and total of 75578 labels.

Final Helmet Detection

## Overview

Final Helmet Detection is a dataset for object detection tasks - it contains Helmet TD7A PJRw S3mj 0Rl7 T4ab 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).

The dataset has 3 classes

1. No person detected
2. Person is present but no helmet
3. A person with helmet

This Dataset can be used to train Deep learning models (like CNN) or to fine tune per-existing architectures for detection of helmet of a bike rider.

This prediction can further be used to perform actions such as warn the rider if he/she don't have a helmet

Content

YOLOv3 is a the fastest model to detect an object. This dataset contains weights file trained with YOLOv3 and helmet images. It also contains cfg and names file that can be easily used with OpenCV to detect helmets in images.

Helmet Detection Analytics Market Outlook

According to our latest research, the global Helmet Detection Analytics market size in 2024 stands at USD 1.13 billion, with a robust year-on-year growth trajectory. The market is experiencing a significant expansion, driven by a compound annual growth rate (CAGR) of 17.2% from 2025 to 2033. By the end of 2033, the market is anticipated to reach an impressive USD 4.12 billion. This remarkable growth is primarily attributed to the increasing emphasis on workplace safety regulations and rapid advancements in artificial intelligence and video analytics technologies, which are transforming safety compliance across industries such as construction, manufacturing, mining, and transportation.

One of the primary growth drivers for the helmet detection analytics market is the stringent enforcement of occupational safety regulations globally. Regulatory bodies such as OSHA in the United States, the European Agency for Safety and Health at Work, and similar organizations in Asia Pacific are mandating the use of personal protective equipment (PPE), including helmets, in high-risk sectors. Organizations are under mounting pressure to ensure compliance, not just to avoid hefty penalties but to foster a culture of safety. Helmet detection analytics solutions, leveraging advanced AI and video analytics, enable real-time monitoring and automated compliance reporting, which significantly reduces the risk of workplace injuries and fatalities. This regulatory push, combined with increasing awareness about employee safety, is compelling enterprises to invest in smart safety solutions, thereby fueling market growth.

Technological advancements are another critical factor propelling the helmet detection analytics market forward. The integration of artificial intelligence, deep learning algorithms, and IoT-enabled sensors has revolutionized the way helmet usage is monitored in hazardous environments. Modern helmet detection systems can now offer high accuracy in identifying non-compliance, even in complex and dynamic work settings. Additionally, the proliferation of edge computing and cloud-based analytics platforms is enabling scalable and cost-effective deployment of these solutions. As a result, both large enterprises and small to medium-sized businesses are increasingly adopting helmet detection analytics to enhance operational efficiency and ensure regulatory adherence. The continuous evolution of video analytics and sensor technologies is expected to further expand the market’s capabilities and adoption rates.

The surge in digital transformation initiatives across industries is also contributing significantly to the growth of the helmet detection analytics market. Organizations are increasingly embedding smart safety solutions into their broader digital ecosystems to achieve real-time visibility into safety compliance and incident management. The demand for integrated safety analytics platforms that can seamlessly interface with other enterprise systems such as human resource management, incident reporting, and access control is on the rise. This integration allows for holistic safety oversight and data-driven decision-making, which is particularly valuable in sectors like mining, oil & gas, and manufacturing where operational risks are high. As companies continue to prioritize digitalization and smart workplace strategies, the adoption of helmet detection analytics is expected to accelerate further.

From a regional perspective, Asia Pacific is emerging as the fastest-growing market for helmet detection analytics, driven by rapid industrialization, increasing construction activities, and stringent government regulations regarding worker safety. North America and Europe remain dominant markets due to their mature regulatory frameworks and early adoption of advanced safety technologies. Meanwhile, the Middle East & Africa and Latin America are witnessing steady growth, propelled by infrastructural development and rising investments in occupational safety. Regional market dynamics are influenced by the pace of technology adoption, economic development, and the regulatory environment, with each region presenting unique opportunities and challenges for market players.

Rider Helmet Detection

## Overview

Rider Helmet Detection is a dataset for object detection tasks - it contains Helmeted annotations for 3,146 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).

IITU Safety-Helmet Dataset v1.0 Demo

  Overview:

This dataset contains annotated images of safety helmets captured both by drone and at ground level, designed for helmet detection and color classification tasks in computer vision. This is the DEMO version of the dataset, now it contains only 14 images and annotations.

  📖 Dataset Summary

This dataset contains 1,664 images annotated for safety-helmet detection and color classification.

6,473 helmet instances… See the full description on the dataset page: https://huggingface.co/datasets/ersace/IITU_Safety-Helmet_Dataset_v1.0_Demo.

Rahul Helmet Detection

## Overview

Rahul Helmet Detection is a dataset for object detection tasks - it contains Helemts Numberplate annotations for 498 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 via Vision for Riders Market Outlook

According to our latest research, the Global Helmet Detection via Vision for Riders market size was valued at $415 million in 2024 and is projected to reach $1.12 billion by 2033, expanding at a CAGR of 11.5% during 2024–2033. The primary driver fueling this market’s robust growth is the increasing global emphasis on rider safety, coupled with the rapid adoption of artificial intelligence (AI) and computer vision technologies. As urbanization accelerates and the number of two-wheeler riders surges worldwide, regulatory bodies and enterprises are prioritizing advanced safety solutions to reduce road fatalities and workplace accidents. Helmet detection via vision systems, leveraging AI and sensor fusion, are increasingly being integrated into traffic management, industrial safety protocols, and public safety initiatives, positioning this market for substantial expansion over the forecast period.

Regional Outlook

North America currently holds the largest share of the Helmet Detection via Vision for Riders market, accounting for approximately 38% of the global revenue in 2024. This dominance is attributed to the region’s mature technological infrastructure, high adoption rates of advanced driver-assistance systems (ADAS), and stringent safety regulations enforced by government agencies such as the National Highway Traffic Safety Administration (NHTSA). The presence of leading technology innovators and established helmet manufacturers, along with widespread implementation of smart city projects, has accelerated the integration of AI-based helmet detection solutions in both urban and industrial environments. Moreover, robust funding for research and development, coupled with proactive policy measures, has further cemented North America’s leadership in this sector.

The Asia Pacific region is projected to be the fastest-growing market, with a forecasted CAGR of 14.2% from 2024 to 2033. This rapid expansion is driven by the burgeoning population of motorcyclists, increasing urbanization, and rising awareness of road safety across countries such as India, China, and Indonesia. Governments in these countries are implementing stricter helmet laws and investing in smart traffic monitoring infrastructure, which is boosting demand for vision-based helmet detection systems. Additionally, significant investments from both public and private sectors in AI and IoT technologies, as well as the emergence of local startups specializing in computer vision, are catalyzing market growth. The region’s dynamic economic landscape and focus on reducing traffic-related injuries are expected to sustain this upward trajectory throughout the forecast period.

Emerging economies in Latin America and the Middle East & Africa are experiencing gradual adoption of helmet detection via vision technologies, primarily due to localized demand for enhanced road and workplace safety. However, these regions face challenges such as limited infrastructure, budget constraints, and varying enforcement of safety regulations. Despite these hurdles, there is a growing recognition of the value of AI-driven safety solutions, particularly in urban centers and industrial zones. International collaborations, pilot projects, and government-led awareness campaigns are gradually overcoming adoption barriers, paving the way for future market growth. As these regions continue to urbanize and prioritize safety, the adoption of helmet detection systems is expected to accelerate, albeit at a more measured pace compared to North America and Asia Pacific.

Report Scope

This dataset has been specifically created to aid in the development of intelligent systems for detecting helmet usage by motorcycle drivers and passengers. It is suitable for training deep learning models such as YOLOv8 for real-time object detection in road safety and traffic monitoring applications.

The dataset contains custom-labeled images that capture various helmet-related scenarios in traffic environments, including:

✅ Driver with Helmet

❌ Driver without Helmet

✅ Passenger with Helmet

❌ Passenger without Helmet

🛵 Bike

🧍 Driver (General)

🧍 Passenger (General)

Each image is annotated using bounding boxes and labeled in YOLO format, making it directly compatible with models like YOLOv5, YOLOv8, etc.

Helmet Detection

## Overview

Helmet Detection is a dataset for object detection tasks - it contains Helmets annotations for 715 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 [MIT license](https://creativecommons.org/licenses/MIT).

Enhance road safety with advanced helmet detection using YOLOv8, the cutting-edge in object detection models. Our solution leverages the power of deep learning and computer vision to accurately detect riders and determine helmet compliance in real-time. Built on YOLOv8 by Ultralytics, this system offers unparalleled speed and precision, making it ideal for traffic monitoring and safety enforcement applications.

By utilizing the latest advancements in machine learning, our helmet detection model processes live video feeds to identify motorcyclists and assess whether they are wearing helmets. This technology is crucial for promoting motorbike safety, reducing accidents, and ensuring adherence to traffic safety regulations.

##### Key Features:

Real-Time Detection: Instantly detect riders and recognize helmet usage with high accuracy. Advanced Object Detection: Powered by YOLOv8, offering superior performance in various conditions. Scalable Solution: Easily integrates with existing computer vision systems and supports customization. Open-Source Framework: Developed in Python, leveraging open-source technologies for flexibility. Enhanced Safety Compliance: Assists authorities in monitoring and enforcing helmet laws effectively. Implementing our helmet detection system can significantly contribute to safer roads by ensuring riders comply with essential safety measures. Harness the capabilities of YOLOv8 and artificial intelligence to make a positive impact on traffic safety today.

Keywords: Helmet Detection, YOLOv8, Rider Detection, Computer Vision, Deep Learning, Machine Learning, Object Detection, Real-Time Detection, Safety Compliance, Traffic Safety, Motorbike Safety, Artificial Intelligence, Python, Open-Source, Ultralytics