Computer Vision
ALGOFACT builds computer vision systems for manufacturing and operations sites that detect defects, count products, check safety rules and read text with OCR. We train and validate the model on real images from your line, then run it on an edge device next to the line or in the cloud, depending on latency and network needs. An operator screen, alert rules and an image archive for retraining are part of the delivery. Engineering runs from our offices in Türkiye.
Last updated:
If a check is done by eye today, repeats all day and can be described visually, it is a strong candidate for computer vision: a surface scratch, a missing part, a wrong label, a worker entering an area without a helmet, a lot code that has to be read. A camera applies the same standard to every unit on every shift and records the result together with the image.
If a defect is hard to spot even for a trained inspector, the product changes constantly or lighting and angle cannot be fixed, we recommend a feasibility study first. Real line images are collected over a period of time, we measure whether defects can be told apart in the image, and the go or no-go decision is made together on that result. That is how you avoid models that look good in the lab and fail under the lighting on the shop floor.
The most common tasks in manufacturing projects are quality or safety rules that can be handed over to a camera.
Where the model runs depends on how fast the decision must be made and whether images may leave the plant.
If the decision must be immediate, the line must keep working through an internet outage or images must not leave the plant, the model runs on a GPU-equipped industrial PC beside the line. Only results and sample images are sent to the central system.
If images from several plants are processed together, the decision can wait a few seconds or the workload is compute-heavy, the model runs in the cloud. Bandwidth and image storage costs are estimated up front.
In most manufacturing projects the decision is made at the line while training, the image archive and reporting run centrally. New model versions are rolled out to edge devices in a controlled way, with rollback to the previous version.
Most of a model’s quality comes from image quality. We settle camera position, resolution, lens and lighting during feasibility and tell you, based on measurement, whether your existing cameras are good enough.
Often, yes. IP cameras and industrial cameras can be connected as image sources. If resolution, angle or lighting is not enough to show the defect, we measure that during feasibility and recommend additional cameras or lighting where needed.
Yes, but the method changes. With few defect samples, anomaly detection methods that learn what a good unit looks like and flag deviations can be used. Which method is good enough becomes clear from the measurements in the feasibility stage.
Stopping the line is tied to a threshold agreed on from pilot results. At first the system diverts the unit or alerts the operator; automatic actions are switched on step by step as confidence grows.
Safety and line monitoring cameras can process personal data under the GDPR or Türkiye’s KVKK. On the technical side we apply measures such as face blurring, short retention, restricted access and a model design that does not identify individuals. Notices and the legal assessment are handled with your counsel.
The number of stations and cameras, the variety of defect types, labeling volume, edge hardware needs, PLC or MES integration and the length of the pilot. Along with the feasibility results we present a phased scope and the hardware list in writing.
Describe the problem briefly and we will reply with a time for a 30-minute discovery call.
Book a discovery call