Image analysis (computer vision)
Computer vision put to practical use: recognition, quality control, inspection and video stream analysis.
What is it, in practice?
Computer vision lets software understand what is in an image or a video: recognise an object, detect a defect, read text, count, measure. It is AI applied to sight, used for quality control, inspection and analysis.
Where people tire, lose focus or run out of time, a vision model analyses every image without a break, at the same pace and with the same level of attention. On a production line, a camera feed or an archive collection, it picks out what needs picking out.
Computer vision is not learned in the abstract: a model is trained on examples close to your reality. The more the training cases resemble your conditions on the ground, the more accurate the detection. That is why a vision project always starts with your own images.
How it works
Define what needs to be seen
We pin down exactly what the model has to detect: a defect, an object, a presence, a piece of text.
Build an image dataset
We collect and label examples that reflect your real conditions: lighting, angles, the full range of cases.
Train and measure
The model is trained, then tested on images it has never seen, so its reliability is measured honestly.
Plug it into the workflow
The model is connected to your cameras or files, and its detections feed into a dashboard your teams can use.
When should you use it?
- Detect defects on a production line automatically
- Count and track objects or people
- Read and extract text from images (OCR)
- Analyse video streams in real time
What we deliver
- Vision model trained on your cases
- Integration with your existing cameras or feeds
- Detection dashboard
Every project starts with scoping your needs and checking feasibility, before a single line of code.