AI PPE Detection: Using Cameras to Check Hard Hats, Hi-Vis and Safety Gear on Site
AI PPE detection spots missing hard hats, hi-vis vests and safety glasses on camera, alerts on site and logs each event. Set-up steps and privacy design.
9 min read
Computer Vision Systems
What Does a Computer Vision System Do?
A computer vision system analyses camera feeds with AI as they arrive: it rejects defective items, counts products, reads labels and number plates, and checks that hard hats and hi-vis vests (PPE) are worn. We build these systems to fit your site, work with existing RTSP/ONVIF IP cameras and, where needed, install cameras, lighting and processing units.
Footage nobody watches catches nothing. Most sites record every camera, yet the footage only gets reviewed after something has gone wrong. A defective part ships, a missing hard hat goes unnoticed, a short pallet is discovered by the customer. Paying people to watch screens all shift is costly, and attention fades long before the shift ends.
Computer vision hands that watching to software. The camera feed is analysed as it arrives, and when a condition you have defined occurs, the system rejects the item, raises an alert or logs the event. We build custom systems for object detection, classification, anomaly detection and optical character recognition (OCR). We can work with the cameras you already have, or supply and install cameras, lighting and processing units on site.
Where Computer Vision Earns Its Keep
Production Line Quality Control
Problem: Visual inspection slips as the shift wears on and can't keep pace with the line, so defects reach customers.
Solution: Every item on the conveyor is imaged. The model spots scratches, cracks, colour deviation and shape faults; rejects are diverted and the defect type is logged.
Counting & Dispatch Verification
Problem: Counting shelves, pallets or cartons by hand is slow, and short shipments surface only as complaints.
Solution: Items are counted from the camera feed and checked against the order. Any mismatch is flagged before loading is complete.
Label, Barcode & Document Reading
Problem: Keying in label, delivery note and invoice data by hand takes time and introduces typos.
Solution: Text, barcodes and QR codes are read from labels and documents and passed straight into your ERP or warehouse software.
Health & Safety (PPE) Monitoring
Problem: Hard hat, hi-vis and eye protection rules are only enforced while an inspector is on the floor.
Solution: PPE is checked continuously in defined zones; gaps trigger an alert and each breach is reported with its image.
Vehicle & Number Plate Recognition
Problem: Vehicle movements rely on a paper log or on the attention of the barrier operator.
Solution: Plates are read from the camera image, matched against an authorised list and the barrier opens automatically. Every passage is logged with a timestamp.
Occupancy & Restricted-Area Alerts
Problem: Nobody knows in real time how many people are in an area or who has entered a restricted zone.
Solution: Occupancy is calculated and entry into restricted zones triggers an instant alert. Where face recognition is needed, it is designed around biometric data obligations under Turkey's KVKK data protection law.
If you also need card or QR-based access for staff and visitors, take a look at SmartPass, our access control system. To extract data from scanned paperwork, see document OCR.
Technical Stack
- YOLO and EfficientDet-based object detection
- OpenCV, TensorFlow and PyTorch
- Edge devices on site (NVIDIA Jetson, Raspberry Pi)
- Real-time analysis sized to your line speed
- IP camera connectivity (RTSP / ONVIF)
- On-premise or cloud deployment
- Models trained on images of your own products
- REST API links to ERP, MES and PLCs
How a Project Runs
01
Site Survey & Data Capture
We pin down the defect or event to detect, check camera angles and lighting on site, and collect training images.
02
Training & Testing
The model is trained on images of your products, then accuracy, speed and hardware needs are measured on test footage from your site.
03
System Integration
The model is connected to your cameras and production systems, with reject, alert and reporting flows set up.
04
Go-Live & Monitoring
Once live, false alarms and missed detections are tracked and the model is updated with new images.
Start With the Cameras You Have
If your IP cameras support RTSP or ONVIF, there is usually no need to buy new ones to get started. Where an angle or resolution is not good enough for reliable detection, the survey will say so and we recommend extra hardware only for those points.
Questions we hear most often
Frequently Asked Questions
What is a computer vision system used for?
A computer vision system analyses camera feeds with AI as they arrive. When a condition you have defined occurs, it rejects the item, raises an alert or logs the event. Typical uses are production-line defect detection, product counting and shipment checks, label and barcode reading, PPE monitoring, number plate recognition and occupancy monitoring.
Can we use our existing IP cameras?
If your IP cameras support RTSP or ONVIF, you can usually start without buying new ones. If a camera's angle or resolution is not good enough for detection, the site survey will show it and we recommend extra hardware only where it is needed. For precise measurement or fast lines, we can supply and install cameras, lighting and processing units.
How does AI quality control with cameras work?
Every item passing on the conveyor is imaged. The model detects scratches, cracks, colour deviations and shape defects; a defective item is rejected and the defect type is logged. The model is trained on images of your own products, analysis is sized to line speed, and results can be passed to ERP, MES or PLC systems through a REST API.
Can number plate recognition open a barrier?
Yes. The plate is read from the camera image, matched against the list of authorised vehicles and the barrier opens automatically. Every pass is logged with its time, so vehicle entry and exit no longer depend on a handwritten log or the attention of a barrier attendant.
What should we consider before using facial recognition?
Face images are biometric data, which Turkish data protection law (KVKK) treats as a special category of personal data. If facial recognition is needed, the system is designed around those obligations from the outset. Many tasks, such as occupancy monitoring or restricted-zone alerts, can be handled without facial recognition at all.
How is a computer vision model trained?
Sample images from your site are labelled, the model is trained on them and accuracy is measured on separate test images. We use YOLO and EfficientDet-based object detection with OpenCV, TensorFlow and PyTorch. Images the live system gets wrong feed into the next training round, so the model improves over time.
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