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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: when a condition you have defined occurs, the system rejects the item, raises an alert or logs the event. On a packaging line, for example, a carton with a missing or misprinted label is diverted before it leaves the conveyor, and because the image and time are logged you can also see which shift and which machine the problem started on. We train object detection, classification, anomaly detection and optical character recognition (OCR) models on images of your own products, so your quality team stops checking every item by eye and focuses on the cases the system flags.
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.
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.
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.
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.
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.
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.
For barrier-controlled vehicle entry, see our number plate recognition systems; to watch site cameras from a single screen, see IP camera monitoring. If you also want card-based access for staff and visitors, with time and attendance and canteen records in the same panel, take a look at SmartPass, our access control system; to buy SmartPass, simply fill in the sales form. To extract data from scanned paperwork, see document OCR.
We pin down the defect or event to detect, check camera angles and lighting on site, and collect training images.
The model is trained on images of your products, then accuracy, speed and hardware needs are measured on test footage from your site.
The model is connected to your cameras and production systems, with reject, alert and reporting flows set up.
Once live, false alarms and missed detections are tracked and the model is updated with new images.
For more detail, read our guides on setting up computer vision quality inspection step by step, detecting hard hats, vests and PPE with cameras and automating fabric defect detection with AI. More articles are collected in our AI guides for business.
Will it be accurate enough? We measure that before installation: the model is trained on images of your products, tested on footage from your site, and the results are shared with you before go-live.
Does footage leave the site? It doesn't have to. Analysis can run on an edge device on site or on your own server; the cloud is used only if you choose it.
Do we have to change the whole plant at once? No. You can start with a single line or checkpoint and expand once you have seen the results.
Tell us which defect or event you want to catch and we will assess feasibility together during the survey. Book a survey or call 0850 346 41 09.
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.
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.
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.
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.
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.
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.
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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