Workplace Safety Monitoring

Helping safety teams monitor personal protective equipment (PPE) across active workspaces is what Sentinel, a computer-vision system, was built to do.

Images, videos, and live camera feeds get analysed to detect workers, safety helmets, and protective vests. When a worker appears to be missing required equipment, Sentinel flags the situation for attention.

Nobody should have to watch every camera every second — computer vision can provide that additional layer of continuous awareness instead. That was the whole idea.

The challenge

Safety monitoring on a busy worksite is rarely straightforward.

Several camera feeds, many workers, changing light, obstructed views, background movement, and activity happening in different areas simultaneously — all of it can be in play at once. Manual monitoring helps, but maintaining constant attention across a large environment is difficult.

PPE compliance is a practical issue in this context. A missing helmet or safety vest can be easy to overlook in a live feed, especially for someone juggling several other tasks.

Continuously analysing visual information and drawing attention to possible violations is how Sentinel supports safety teams in that environment.

Detecting what matters

An object-detection model trained to recognise key workplace-safety objects powers the system:

• Workers
• Safety helmets
• Safety vests

The compliance workflow builds on these detections. Sentinel doesn’t stop at identifying objects in an image — it evaluates spatial relationships to determine whether a worker has the required protective gear.

The core processing flow is:

Image or camera feed → Object detection → Worker–PPE matching → Compliance check

From an object to a violation

Whether a site is compliant isn’t answered by a helmet detection alone. The equipment needs to be associated directly with the person wearing it.

Detected objects get processed to flag specific scenarios:

• A worker without a visible helmet.
• A worker without a visible safety vest.
• A worker missing multiple required items.

Safety officers can review the situation through the application and determine appropriate action once these results surface.

The model provides an automated signal. It does not replace human judgement or site-specific safety procedures.

Monitoring multiple areas

Different parts of a workplace can be monitored through a central workflow because Sentinel supports multiple camera sources.

Feeds process and present together, giving users a site-wide view of PPE compliance. Rather than treating each camera as a separate tool, a shared layer for detection and violation reporting emerges.

That makes scaling from a single-camera demonstration to an integrated multi-point monitoring setup straightforward.

A complete detection pipeline

The end-to-end engineering needed to make the computer-vision system practical was part of the project:

• Preparing and configuring PPE-specific dataset annotations.
• Training and evaluating object detection models.
• Optimising inference pipelines for images, video files, and RTSP live streams.
• Connecting raw bounding box detections to custom compliance logic.
• Handling multi-camera input streams.
• Surfacing real-time visual alerts and potential violations.
• Building intuitive web and desktop interfaces.

Built for real-world conditions

Clean or predictable footage is rarely what a workplace produces. The detection workflow was tested against harsh real-world constraints:

Training with GPU support

Configuring datasets, training models, evaluating detections, and running real-time inference are all supported by Sentinel’s workflows.

Where compatible hardware is available, GPU and CUDA acceleration can speed up both training and deployment, enabling high-frame-rate processing across multiple camera feeds simultaneously.

Web and desktop applications

Two client interfaces, sharing the same detection engine, made the system available:

• Web Interface: Browser-based platform ideal for remote monitoring, viewing consolidated feed alerts, and reviewing uploaded media.
• Desktop Application: Native local interface designed for zero-latency local processing, site deployments with limited internet connectivity, and live hardware demonstrations.

The workflow in practice

A typical monitoring cycle operates as follows:

Camera / Upload → Worker Detection → Helmet & Vest Detection → Compliance Analysis → Potential Violation Alert → User Review

Responsibilities stay clean in this structure: the computer-vision layer identifies what’s visible, the compliance logic interprets relationships, and the application presents structured data for human review.

Outcome

An isolated object-detection experiment turned into a full-stack safety application — that’s the transformation Sentinel went through.

Detecting workers and protective equipment across live camera feeds, while supporting flexible web and desktop workflows, gave the first release a solid foundation.

See the situation. Identify the missing equipment. Bring it to someone’s attention.

The thinking behind the product

Technology can’t remove every risk from a workplace, but it can help safety teams notice potential problems sooner and monitor more consistently. A vital visual support layer is what Sentinel adds where protective equipment matters most.

Project highlights

• Built a PPE compliance monitoring system in 8 weeks.
• Developed real-time detection for workers, helmets, and safety vests.
• Implemented worker-to-equipment spatial matching logic.
• Supported image, video, and live-camera stream analysis.
• Enabled multi-camera feed aggregation.
• Created end-to-end model dataset preparation, training, and inference pipelines.
• Enabled CUDA/GPU acceleration for high-performance processing.
• Built dual Web and Desktop application interfaces.
• Integrated computer vision, compliance rules, stream handling, and alert interfaces into one platform.