CyberFreezeDev
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Camera feeds and photos turned into counts, alerts and pass or fail decisions.
Vision software counts, finds, reads or compares. The real work is fitting models to your lighting, angles and defects, then running them near the camera.
Computer vision is software that reads images. For a business that usually means one of a few concrete jobs: count things (people at a counter, cartons on a conveyor, vehicles at a gate), find things (a missing helmet, a stain, a crack, an empty shelf slot), read things (a label, a meter, a number plate) or compare things (this photo against the approved sample).
The models that do this are well established. The work is in fitting them to your site: your lighting, your camera angles, your products and what your team means by a defect. A detector trained on public datasets knows what a person or a car looks like. It does not know what a bad weld on your product looks like until it has seen a few hundred labelled examples from your line.
Running the model close to the camera matters too. Sending every frame from every camera to the cloud is expensive and slow. An edge device such as an NVIDIA Jetson or a Raspberry Pi with a Coral accelerator can run detection on site and send only the result: a count, an alert, a cropped image of the problem. See edge AI development for the hardware side.
A camera over the line flags scratches, missing parts and misprints, saving each image.
A fixed camera over a conveyor or workstation flags parts with visible defects (scratches, missing components, wrong labels, misprints) and saves the image for a supervisor. See manufacturing.
People, vehicles or items counted, with an alert when a queue gets long.
People at a store entrance or service counter, vehicles at a loading bay, items on a pallet. Counts go to a dashboard or trigger a message when a queue gets long.
Missing helmets or vests, or someone near a running machine, flagged with the frame.
Detect missing helmets or vests in marked zones, or a person entering an area while a machine is running, and alert the supervisor with the frame.
Shelf photos from staff phones checked for empty slots and wrong placement.
Photos of shelves taken by staff on a phone are checked for empty slots and wrong placement against the planogram.
Labels, batch codes, meters and number plates read and checked against rules.
OCR on product labels, batch codes, utility meters and number plates, combined with validation rules. This overlaps with our document intelligence work.
Cameras and lighting checked. Moving a camera or adding a light is often cheapest.
We look at the camera positions, lighting and the actual objects, and collect sample footage or photos. Often the cheapest improvement is moving a camera or adding a light.
A small labelled set shows whether your task is learnable before a large spend.
We label a small set and train a first model to see whether the task is learnable from your images at the accuracy you need. If it is not, you hear it at this stage, before a large spend.
Images across shifts, seasons and rare defects, with labels reviewed by your experts.
Gather enough images to cover day and night, seasons, product variants and the rare defects. Your experts review the labels, because the model learns their judgement.
Precision and recall on held-out images from your site, with examples of every mistake.
Train, then report precision and recall on a held out set from your site, with examples of every kind of mistake, so you can decide whether missed defects or false alarms cost more.
Exported to ONNX or TensorRT and wired to dashboards, WhatsApp alerts or a database.
Export to ONNX or TensorRT, install on the device or server, connect to cameras and to wherever results go: a dashboard, WhatsApp alerts, a PLC signal through your integrator, a database.
Low-confidence cases logged for review, and the model retrained when products or cameras change.
New products, new packaging or a moved camera change what the model sees. We log low confidence cases for review and retrain when needed.
We prefer counting over identifying, blur faces where possible, keep footage for a set period and do not build face recognition for tracking people.
Cameras that capture people collect personal data. For projects in India we design around the DPDP Act 2023: collect only what the task needs, prefer counting and detection over identifying individuals, blur faces in stored images where possible, show clear notices and keep footage for a defined period. For UK and EU sites the same design supports UK GDPR and GDPR duties. We do not build face recognition systems for tracking individuals.
Running inference on an edge device also helps here: raw video can stay on site, and only counts or cropped images of the defect leave the building.
Often yes, if they provide an RTSP stream and the resolution and angle show the objects clearly enough. For inspection tasks a dedicated camera with fixed lighting usually works far better than a ceiling CCTV view.
For a common object in a fixed setting, a few hundred labelled images can be enough to start. Rare defects need more effort, sometimes collected over weeks. The feasibility test tells you how far a small set gets.
No. We build the vision software and connect its results to your systems. If a result needs to move a machine, we hand a signal to your automation integrator.
Not for detection when it runs on an edge device. A connection is needed only to send alerts or results and to receive model updates.
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