Industry Challenge: Ensuring Safety Compliance on Worksites

In hazardous environments like construction and industrial sites, enforcing helmet use is critical to prevent head injuries. Yet one of the most common safety violations is workers failing to wear their helmets, which can lead to serious injuries and liabilities. Traditional helmet compliance checks face major limitations:

• Manual Inspections: Slow, labor-intensive, and prone to human error.

• No Real-Time Oversight: Gaps between inspections mean violations can be missed at the moment.

• Limited Scalability: It’s impractical to monitor large or multiple sites with human inspectors alone.

• Operational Inefficiencies: Manual checks increase costs and delay incident response.

  
  

Not surprisingly, head injuries make up a significant share of workplace accidents (nearly 6% of non-fatal workplace injuries in 2020)(construction-today.com) , underscoring the need for better helmet compliance solutions. To modernize safety enforcement, businesses are turning to AI-powered computer vision for real-time, automated monitoring. However, building and integrating a custom AI solution from scratch can be complex without the right tools.

Tuba.AI: Empowering Your Team to Build Safety AI Workflows

Tuba.AI is a no-code/low-code platform that lets you design, train, and deploy AI vision workflows in minutes. With an intuitive drag-and-drop interface, your safety, operations, and engineering teams can:

• Label Data Automatically: Auto-label helmets and people in images with a click.

• Train Custom Models: Choose between accuracy or speed, pick your framework (PyTorch), and export in ONNX, OpenVINO, TensorRT, or quantized formats, no coding needed.

• Deploy Anywhere: One-click deploy to edge devices or the cloud for real-time helmet detection on live camera feeds.

• Integrate Seamlessly: Plug the generated model into your existing safety dashboards or monitoring systems via standard APIs or model files.

• Scale & Iterate Fast: Add new PPE classes or expand to multiple sites by simply uploading new data and retraining, no pipeline rebuild.

Tuba.AI puts your team in control of the end-to-end AI vision workflow, so you can focus on preventing incidents rather than wrestling with infrastructure.

Use Case Overview: AI Vision Workflow for Helmet Detection

With Tuba.AI, technical and non-technical teams can collaborate to build end-to-end helmet detection workflows by:

• Training custom helmet/person detection models

• Deploying models on edge devices or in the cloud

• Automating compliance alerts in real time

• Embedding AI into existing monitoring platforms

In this use case, we used Helmet Detection Dataset to demonstrate how Tuba.AI can leverage AI to automate helmet detection workflow through an intuitive, no-code interface.

Key Steps using Workflow on Tuba.AI

1. Data Labeling:

Using Tuba’s auto-labeling tool, we efficiently annotate the helmet and human associated with each one in our dataset, reducing the manual effort required for data labeling.

We simply upload the images to the auto-labeling tool and specify the classes to detect, in our case it’s (helmet,person). And start auto-labeling, within minutes the data is labeled and can be checked.

2. Model Training:

The labeled dataset is used to train a helmet and person detection model within Tuba’s no-code environment. The training process is optimized to recognize each person with his associated helmet with high accuracy.

Selecting the training type is the same as labeling tool “detection”, We simplified the process by enabling automatic operation. Alterantively, Users can select manual operation and select the required parameters.:

• Accuracy vs Time: Choose between highest accuracy or faster training. The system allows users to adjust the balance between training accuracy and time, helping to fine-tune the model for optimal performance.

• Framework: The Framework to use PyTorch is the default for its reliability and community support.

• Export Format:  When selected in Framework, users get to choose to what degree quantization should be applied to the weights of the model; float 16, float 32, integer 8.

• Accuracy: Users are allowed to choose which model to choose, the one with the best accuracy or the one with the optimum accuracy, choosing optimum will ensure that the model generalizes better.

• Time: Choose the maximum time to take during training.

3. Deployment

After training, the model is ready for deployment. It can be deployed to edge devices or cloud infrastructure, allowing real-time helmet detection on live video feeds from surveillance cameras or other monitoring systems.

4. Inference and Performance Evaluation

Once deployed, the AI model analyzes incoming video footage in real time to detect whether workers are wearing helmets. The system automatically generates alerts when violations are detected, allowing safety officers to take immediate action. The model’s performance is continuously evaluated to ensure accuracy and reduce false positives.

5. Integration and Scalability

The helmet detection workflow built with Tuba.AI can easily integrate into existing safety monitoring systems. Additionally, it’s scalable, meaning businesses can expand the system to monitor other protective gear (such as vests and goggles) by simply updating the dataset and retraining the model.

Results of the Use Case

During model training on the Helmet Detection Dataset:

• Initial mAP: 25% — a typical baseline

• Final mAP: 99% — achieved near-perfect detection accuracy

  
  

As shown in the above chart, the model started training with 25% mAP which reflects the number of right detection across all the classes.

The model finalized with 99%, ensuring almost no error in such critical cases.

The model successfully identified helmets in varying lighting conditions, angles, and worksite environments, confirming strong model generalization. Visual tests showed high confidence in distinguishing helmet violations in real-world scenarios.

Business Impact for Safety Managers and Industrial Operators

Revolutionize Your Safety Monitoring Process with Tuba.AI

Tuba.AI empowers companies to embed AI-driven helmet detection into their operations quickly and effectively. Key advantages of Tuba.AI include:

• No-Code AI Vision Platform:

  Build robust helmet detection models without writing a single line of code. Tuba.AI’s intuitive drag-and-drop Workflow Builder enables safety teams and non-AI experts to develop end-to-end solutions quickly and independently.

  
  

• Fast Solution Building

  Go from raw images to a deployed model in days, not months. With features like auto-labeling and one-click training, Tuba.AI compresses the development cycle dramatically.

  
  

• Real-Time Performance at the Edge

  Deploy the trained model to edge devices (CCTV cameras, on-site servers) for immediate, on-location analysis. This means you get instant alerts on safety violations as they happen, which is critical when every second counts in preventing accidents.

  
  

• Easy Iterations & Scalability

  Need to improve or expand the model? Tuba.AI makes it easy to iterate – just gather new data, retrain or fine-tune the model within the same workflow. No need to rebuild the pipeline from scratch.

  
  

• Seamless Integration

  Integrate smoothly with your existing systems. Whether your safety platform runs in the cloud or on-premises, the AI module can be plugged in without hassle. Tuba.AI essentially acts as the AI vision engine behind your application.

Tuba.AI: Your Partner in Proactive Safety in WorkSites!

Tuba.AI is a workflow builder that puts your team in control of the AI. This means you create and own the vision solution tailored to your specific needs. From construction-tech software firms to industrial facility operators, any organization can use Tuba.AI to embed AI-powered helmet detection quickly, reliably, and at scale into their safety processes. By using Tuba.AI, you get the flexibility to adapt the AI (add new classes, deploy in new environments) as your requirements evolve, ensuring a future-proof approach to safety compliance.

In essence, Tuba.AI is your behind-the-scenes partner, enabling proactive safety monitoring through AI vision. We handle the heavy tech lifting (model training, optimization, deployment pipeline) so you can focus on leveraging the insights – preventing accidents before they happen and fostering a safer work culture.

Ready to modernize your safety compliance workflow with AI?

Let Tuba.AI help you build the AI vision that powers it all and Sign up now.

Or talk to one of our experts to experience the next level of computer vision solutions

Resources 

Dataset Link : Helmet Detection Dataset