Computer Vision
Visual inspection by a person is thorough for the first hour and tired by the fourth.
Why It Matters
A camera does not lose focus in the last hour of a shift. It checks every unit the same way, flags what matches a known defect pattern, and leaves the final decision to a person who can now spend their attention on the cases that actually need it.
A model built for one specific check does that check well, but it should not be trusted with cases it has never seen. Anything below a confidence threshold, or genuinely new, is flagged for a person rather than passed or rejected automatically.
A model built on a generic dataset will struggle the moment your lighting, camera angle or product finish differs from what it learned on. We collect and label images from your own site, so the model is trained on the conditions it will actually run in.
Every detection carries a confidence level, and the point at which a case is flagged for review rather than passed automatically is agreed with your quality or operations team. This threshold can be adjusted as the model proves itself over time.
A detection can post directly into your ERP or quality system, trigger an alert, or appear on a shop-floor screen, depending on how your team actually operates. The result is not stuck in a separate vision dashboard nobody checks.
Surface marks, misalignment or missing components spotted on a production line as units pass.
Counting items on a shelf or in a bin from a camera feed instead of a manual walk-through.
Reading analog or digital gauges from a camera where no direct data feed exists.
Checking that required safety equipment is visibly worn in a monitored area.
Confirming the right items and quantity are loaded against what was ordered.
Reading printed labels, barcodes or batch numbers directly from a camera view.
We agree exactly what the model should look for and what counts as a pass or a flag.
We gather examples from your site of both normal and flagged cases, and label them.
We train the model and test it against images it has not seen before its accuracy is measured.
The model runs alongside your existing inspection process before it is relied on alone.
It goes live with a review loop, and is retrained as new defect types or conditions appear.
Unit checked, not a sample
Standard across every shift
Defects reaching the customer
Counts and checks completed
Human attention on real exceptions
Evidence for every flagged case
The model learns from your actual site conditions, not a generic dataset that does not match.
We report tested accuracy plainly, and route uncertain cases to a person rather than guess.
The model runs alongside your existing process first, so trust is earned before it runs alone.
Flagged cases and counts can post directly into your systems, not sit in a separate tool.
We use the approach that fits your images and hosting rules, with no lock-in to one vendor.
It is the use of AI to analyse images or video, such as spotting a defect on a production line, counting stock on a shelf, reading a gauge, or checking whether safety equipment is being worn. It replaces a person visually checking every unit with a camera that checks continuously and flags what needs attention.
Accuracy depends on image quality, lighting and how varied the cases are, and we test it against your own images before it runs live. We report accuracy honestly rather than promise a number, and low-confidence cases are flagged for a person rather than decided automatically.
No. It checks every unit for the patterns it was trained to catch and flags the ones that need a closer look. A person still makes the final call on flagged cases and on anything the model has not seen before.
Often an existing camera or a standard industrial camera is enough. We assess your current setup first and recommend changes only where the image quality genuinely will not support reliable detection.
It varies by task, but a useful starting model can often be built from a few hundred to a few thousand labelled images of both normal and defective cases. We help collect and label this during the build.
A first working model for one clearly defined check usually takes six to ten weeks, including data collection, labelling, training and testing on your own site before go-live.
Related
The other services in this section, and related pages from TCB Infotech.
No commitment. A practical look at what a model could catch.
