AI-Powered Safety Monitoring System for Manufacturing Environments

Our team designed an AI safety monitoring system specifically for manufacturing facilities where the risks are real and immediate

~5s PPE check

Automated entry validation
24/7 monitoring

Hot zones, exits, and PPE tracked in real time
EU AI Act–ready

Compliance built into the system

About the Project: Advanced Safety for Employees

This is a solution we’ve created for a huge enterprise manufacturing, with 5000+ employees who operate in high-risk environments, such as manufacturing plants with molten materials, chemical processing facilities, large-scale warehouses, or construction sites.

Such big organizations often struggle with the same problem: keeping people safe when manual monitoring just isn’t enough. This project offers a practical answer to that challenge.

The system tackles three specific problems: it watches for people entering dangerous zones when they shouldn’t be there, it makes sure everyone entering the facility is wearing the right protective equipment, and it keeps emergency exits clear and accessible at all times.

Challenges of Developing AI-Powered Computer Vision Systems

Real-Time Detection in Extreme Conditions

These systems work in places with fire, furnaces, glowing metal, steam, and smoke. Heat and bright light make it hard for cameras to “see” clearly. The system still needs to spot a human instantly and trigger an alarm with almost no delay — even in these extreme conditions.
Checking PPE with High Accuracy

The system needs to recognize several different safety items at once. Workers wear different colors and styles, and cameras see them from different angles. If the system overlooks missing PPE (helmets, glasses, vests, masks), people may get hurt. But if it makes too many mistakes, it slows everyone down. So accuracy really matters.
Following EU AI Act Compliance Requirements

Even though the system uses AI for personalized recommendations and analytics, we needed to ensure compliance with the EU AI Act. This meant implementing transparent data processing, giving users control over their information, and documenting how recommendation algorithms work.
Running Everything on Existing Camera Systems

The software runs on the same network and devices the factory already uses. It must work fast, handle many camera feeds at once, and not overload the system. It also needs to decide what to process locally and what to send to central servers to avoid slowdowns.

Solutions We’ve Implemented

Watching the Hot Zones

We used existing CCTV cameras in the most dangerous areas of the plant. These systems learn what "normal" looks like, an empty hot zone during operation hours. When someone walks into frame, the system notices it and acts. Alarms go off in the area. The control room gets an instant notification with live video. Security sees exactly who's there and where. Every incident gets recorded automatically with timestamps and footage, so there's always a clear record of what happened.
Checking PPE Using a Mobile App

Instead of clipboard inspections, workers use their phones or tablets at entry points. Then the camera checks their gear. Helmet? Safety glasses? Vest? Everything required for that specific area. The whole thing takes about five seconds. If everything's good, they get a green light and walk through. If something's missing, they get a red screen, entry is blocked, and their supervisor gets a message immediately.
Keeping Exits Clear

Cameras positioned at every emergency exit and fire equipment station watch for obstructions around the clock. The AI knows what these spaces should look like when they're clear. When something blocks the way, a pallet, equipment, or materials, the system measures how serious the blockage is and sends alerts accordingly.

Unified Monitoring System — One View of Everything

All systems connect to one dashboard, so safety managers can:

See live alerts
Watch recorded incidents
Track trends
Export reports for compliance

Maintenance gets a notification with a photo showing exactly what’s blocking the exit and where. They can fix it before it becomes a real problem, and there’s documentation that it happened and was resolved.

Since everything connects to one dashboard, safety managers see all the alerts. They can pull up any incident with video evidence and generate reports for auditors if requested.

Technologies

Yolo
Python
AWS
Tensor flow

Key Deliverables

Computer Vision Infrastructure

for existing cameras in manufacturing, positioned throughout the facility, in hot zones, at entry points, around emergency exits, all connected and analyzing in real time
Mobile App

that workers actually use because it’s faster than the old way, not because they’re forced to
Monitoring Dashboard

that shows safety managers what’s happening right now and gives them the tools to dig into past incidents
Alert System

that reaches people however they need to be reached, alarms on site, notifications on phones, alerts in the control room
Automatic Documentation

that creates the compliance trail without anyone having to write anything down
Analytics

that help identify patterns, like if the same exit keeps getting blocked, or if certain shifts have more PPE violations

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Summary: Developing Computer Vision for Manufacturing

The problems this system solves exist across manufacturing, chemical processing, construction, and anywhere else people work in proximity to serious hazards.

The architecture adapts (different cameras, different equipment checks, different hazard zones), but the core idea stays the same: use AI to notice problems faster than humans can, and create automatic responses that don’t depend on someone happening to be in the right place at the right time.

At SpdLoad, we build computer vision solutions that address specific operational challenges. This project demonstrates our approach: understand the real problem, then design systems that fit into actual workflows, and deliver technology that makes workplaces measurably safer.