Helmet, Motorcycle rider and number plate detection dataset. Mixed from already available projects added with my own made dataset

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.

Helmet Motorcycle Detection

## Overview

Helmet Motorcycle Detection is a dataset for object detection tasks - it contains Helmet annotations for 1,129 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 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 V3

## Overview

Helmet Detection V3 is a dataset for object detection tasks - it contains Helmet annotations for 9,966 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 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.

Here are a few use cases for this project:

1. Construction Site Safety: The "Helmet Detection" model can be used to enforce safety protocols in construction sites by automatically identifying and alerting supervisors when a worker is not wearing a helmet.

2. Sports Compliance: The model can be applied to detect whether athletes in sports like cycling, motor racing, or American football are wearing helmets during practices or competitions, ensuring compliance with safety rules.

3. Traffic Surveillance: The model can be utilized by traffic surveillance systems for automatic enforcement of helmet laws for motorcyclists and bicyclists.

4. Industrial Safety Monitoring: Companies can use this model in factories and industrial settings to monitor workers' compliance with safety regulations, reducing the risk of head injuries.

5. Emergency Response Evaluation: The model could assist emergency responders by quickly assessing the helmet use of victims in accidents involving bicycles or motorcycles, providing valuable data for medical triage.

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.

Acquired a comprehensive dataset from online sources containing around 120 images with complete rider information, including the rider, helmet presence, and visible number plate and annotate it.

The dataset that aims to enhance road safety by addressing two critical aspects; Helmet Violation Detection and Capturing Bike Numbers

Below is my project implementation of the dataset. If you find this useful kindly upvote it in the github.

https://github.com/aneesarom/Real-Time-Detection-of-Helmet-Violations-and-Capturing-Bike-Numbers-from-Number-Plates

My_Linkedin: https://linkedin.com/in/anees-aro-52a79614a/

🪖 Helmet Detection

## Overview

🪖 Helmet Detection is a dataset for object detection tasks - it contains Objects annotations for 19,577 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).

Context

This dataset is part of the helmet detection project.

Content

It consists of image data. Helmet kept on the table, people wearing a helmet.

Acknowledgements

Thanks to Google, downloaded the data from the internet.

Dataset from - https://github.com/wujixiu/helmet-detection

Used as an example to demonstrate Monk - Open Source Object Detection library: https://github.com/Tessellate-Imaging/Monk_Object_Detection

Sample Example: https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/example_notebooks/1_gluoncv_finetune/Example%20-%20Helmet%20Detection.ipynb

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.

The dataset contains images sourced from google images of bikers with and without helmets. Most of the images are from the Indian subcontinent.

There are about 50 images for both classes: * with * no

Potential use cases: => Training model to identify helmet status from street surveillance cameras

Copyright to the images belong to the respective owners. Images aggregated for educational purposes only.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F793761%2F564029f91771ab7634485efe03e0ae0c%2Fhelmets.png?generation=1591457410673402&alt=media" alt="">

About this Dataset

This dataset contains 764 images of 2 distinct classes for the objective of helmet detection. Bounding box annotations are provided in the PASCAL VOC format The classes are: - With helmet; - Without helmet.

How to use this dataset

• Your kernel can be featured here!
• More datasets

How to Cite this Dataset

If you use this dataset in your research, please credit the authors

BibTeX

@misc{make ml, title={Bikes Helmets Dataset}, url={https://makeml.app/datasets/helmets}, journal={Make ML}}

License

Public Domain

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Photo by viklundvisuals on Unsplash

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.

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

Helmet Detection DATA SCIENCE 4

## Overview

Helmet Detection DATA SCIENCE 4 is a dataset for object detection tasks - it contains Helmet annotations for 2,607 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

  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.

  📖 Dataset Summary

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

6,473 helmet instances
Captured by drone (3–5 m, 10–15 m; angles 0°, 45°, 90°) and at ground level
Lighting… See the full description on the dataset page: https://huggingface.co/datasets/ersace/IITU_Safety-Helmet_Dataset_v1.0.

Helmet Detection via Vision for Riders Market Outlook

According to our latest research, the global Helmet Detection via Vision for Riders market size reached USD 1.21 billion in 2024. Driven by rapid advancements in artificial intelligence and computer vision, the market is projected to grow at a robust CAGR of 18.7% from 2025 to 2033. By the end of 2033, the market is anticipated to reach approximately USD 6.14 billion. This remarkable growth is largely fueled by increasing regulatory mandates for rider safety, the proliferation of smart city initiatives, and the integration of deep learning technologies into surveillance and traffic management systems.

A significant growth factor for the Helmet Detection via Vision for Riders market is the escalating global emphasis on road safety and compliance with helmet-wearing regulations. Governments and regulatory bodies across regions are intensifying their efforts to reduce fatalities and injuries resulting from two-wheeler accidents, which account for a substantial proportion of road traffic incidents worldwide. The deployment of AI-powered helmet detection systems in urban and semi-urban areas is becoming a standard practice, as these solutions enable real-time identification of non-compliance and facilitate prompt enforcement actions. The integration of helmet detection technology into existing traffic surveillance infrastructure not only enhances public safety but also supports data-driven policy formulation, further propelling market demand.

Another key driver is the rapid evolution of computer vision and deep learning algorithms, which have significantly improved the accuracy and reliability of helmet detection systems. Modern helmet detection solutions leverage advanced neural networks and high-definition imaging to distinguish between helmeted and non-helmeted riders under varying lighting and environmental conditions. This technological sophistication has expanded the application scope beyond traditional traffic enforcement, making these systems indispensable in industrial safety, smart city projects, and large-scale transportation networks. The ability to process and analyze vast volumes of video data in real time has also opened up new possibilities for predictive analytics and proactive risk mitigation, attracting investments from both public and private sectors.

Furthermore, the market is witnessing strong momentum from the convergence of Internet of Things (IoT) technologies and cloud computing. Helmet detection systems are increasingly being integrated with IoT-enabled cameras and edge devices, allowing for seamless data transmission, remote monitoring, and centralized management. Cloud-based deployment models have facilitated the scalability and interoperability of these solutions, enabling authorities and enterprises to implement helmet detection across multiple locations with minimal infrastructure overhead. As the demand for connected and intelligent safety solutions continues to rise, the synergy between IoT, cloud, and AI is expected to unlock new growth avenues for market participants.

From a regional perspective, Asia Pacific dominates the Helmet Detection via Vision for Riders market, accounting for the largest share in 2024, followed by North America and Europe. The high incidence of two-wheeler usage, coupled with stringent safety regulations in countries such as India, China, and Indonesia, is driving large-scale adoption of helmet detection systems in the region. North America and Europe are also experiencing steady growth, supported by smart city initiatives, technological advancements, and proactive government interventions. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, with increasing investments in urban mobility and road safety infrastructure.

Component Analysis

The Helmet Detection via Vision for Riders market is segmented by component into hardware, software, and services. Hardware forms the foundational layer of any helmet detection system, encompassing high-resolution cameras, embedded processors, edge devices, and networking equipment. The demand for robust and weather-resistant hardware is particularly high in outdoor deployments, where systems must operate reliably under diverse environmental conditions. Vendors are continually innovating to deliver compact, energy-efficient, and easy-to-install hardware solutions that can be seamlessly integrated with existing surveillance infrastructure. The p