Our Case StudiesProactive Safety Enforcement with AI-Powered PPE and Zone Monitoring.
Softlabs Group developed an AI-based workplace safety system that ensures compliance and reduces risks. Using computer vision and edge computing, the solution detects PPE violations and unauthorized zone access in real time.
Manufacturing, Safety and Compliance
Enterprise
Agile Scrum
.Net Application, Server
FPMcCann, a leading name in the UK construction and manufacturing sector, stands as the backbone of industry reliability and safety. With a legacy of commitment to excellence, FPMcCann leads the way in implementing cutting-edge solutions to ensure workplace safety and operational efficiency.
As they embark on the PPE Detection project, FPMcCann continues to set industry standards for safety and innovation.
UK
Global workplace injury costs exceed $250B annually, highlighting the urgent need for real-time safety systems.
But relying on manual checks is slow and prone to mistakes. FPMcCann recognized this challenge:
they needed a smarter way to keep tabs on safety gear, quickly and accurately. So, they set out to create PPE Detection, an automated solution.
As the AI and software partner, we built a real-time workplace safety monitoring solution to detect PPE non-compliance and control access to restricted areas using AI and computer vision.
The system utilizes object detection models trained to recognize helmets, vests, gloves, and masks. It also monitors zone boundaries through geofencing and camera-based motion detection.
We developed the solution with CCTV integration, YOLO-based AI detection, and a central dashboard that enables real-time alerts, safety logs, and compliance reporting for supervisors and safety officers.
As workers arrive at the facility, each one is required to wear the appropriate PPE before crossing into the operational zone. This initial checkpoint is the foundation of proactive safety enforcement.
CCTV cameras installed at key access points record high-resolution frames of every individual. These visuals are immediately sent to the AI system for real-time inspection.
Powered by YOLOv7, the system rapidly detects individuals and scans for essential safety gear like helmets, vests, and gloves. Its deep learning core ensures fast, accurate identification with minimal false positives.
The system maps each person’s PPE status against a site-specific compliance matrix. This ensures that all required gear is present according to job role and risk classification.
If any PPE item is missing, the system flags the instance and sends alert notifications to supervisors. Alerts include visual evidence, timestamps, and location metadata for rapid intervention.
All incidents and safe entries are recorded in a centralized dashboard. Safety teams can view logs, track trends, and export reports for audits or internal reviews.
By eliminating manual checks, the solution enforces safety continuously and without bias. This automation reduces risk exposure and cultivates a culture of accountability.
Inconsistent manual checks causing missed PPE violations
Unauthorized personnel entering restricted zones
Delayed safety violation reporting and response
Difficulty ensuring safety across large industrial areas
Lack of centralized safety monitoring and logging
Deployed real-time AI models to detect PPE compliance
Integrated zone-monitoring for access control
Enabled live alerts and violation evidence via dashboard
Used edge devices for on-site detection without delay
Centralized data access for safety audits and oversight
Automated safety checks across work zones, reducing oversight reliance.
Live alerts helped security teams respond instantly to violations.
Early intervention decreased unsafe behavior and accident risks.
Management gained full control over site-wide safety enforcement.
Identifies missing helmets, gloves, vests and masks in real time.
Monitors unauthorized movement in red or restricted areas.
Sends violation alerts with photo evidence to supervisors instantly.
Visual logs, trend analysis and exportable safety audit reports.
Real-time PPE and area compliance for high-risk construction zones.
Monitoring workforce compliance across multiple plant sections.
Preventing exposure in hazardous restricted zones.
Detecting unsafe behaviors in busy loading and stocking areas.
Tracking PPE usage in underground and open-pit environments.
React JS
Node.js
MongoDB
YOLOv7
Python
OpenCV
Web-Based Safety Dashboard
Years of Experience
Countries
Clients
Projects
At Softlabs Group, we take pride in solving complex business challenges with innovative and reliable solutions. Our case studies showcase how we’ve empowered clients across industries with tailored software that delivers measurable results and drives success.
Manual checks could not reliably confirm whether quarry trucks were safely covered before leaving the site.
Developed an edge AI system that detects truck covers and connects with boom barriers for automated exit control.
95%+ detection accuracy, automated monitoring, lower compliance risk and deployment in under two weeks.
Manual inventory checks caused errors, delayed stock updates and limited visibility across warehouse operations.
Built a computer vision system that monitors stock movement and provides real-time inventory data through a central dashboard.
98%+ tracking accuracy, live stock visibility, reduced manual work and improved operational efficiency.
Manual monitoring could overlook vibration exposure, worker fatigue and workplace accidents.
Developed an AI-enabled smartwatch that tracks vibration, worker vitals, falls and exposure levels in real time.
Earlier safety warnings, stronger compliance, fewer health disruptions and better exposure visibility.
Golfers lacked accurate, real-time feedback to understand and improve their putting technique.
Developed a compact AI device with motion tracking, stroke analysis and instant feedback through a mobile app.
A functional, market-ready product with personalised training insights and positive early-user feedback.
Manual attendance caused payroll errors and time theft, while safety checks could miss PPE violations.
Built an AI system using existing CCTV for facial recognition, attendance logging and real-time PPE detection.
Reduced attendance fraud, faster check-ins, stronger safety compliance and automated HR and payroll processing.
Information desks faced long queues, repetitive enquiries, language barriers and outdated passenger information.
Built a life-sized multilingual AI concierge using governed RAG and verified airport information.
Zero waiting for general enquiries, 95% accurate resolution, improved passenger experience and lower support workload.
FAQs
Yes, it identifies missing PPE gear in real time using AI-based object detection models.
Yes, it integrates easily with standard IP or analog camera systems already in use.
No. The system processes data on edge devices and only needs periodic connectivity for sync.
Absolutely. You can define multiple detection zones across different areas of your facility.
Violations are logged with time-stamped snapshots and instantly sent via dashboard or email.
Yes, it is trained for varied industrial conditions including low-light and debris-heavy zones.
All data is encrypted, stored securely, and access-controlled for compliance and audit use.
Deployment is typically completed in 2–3 weeks depending on your facility’s layout.
Yes, downloadable reports include timestamps, images, and compliance metrics.
Yes, detection logic, PPE types, and alert workflows are tailored to your operational needs.
