Object Recognition and Computer Vision Development for Real Cameras

Detection finds where objects are in an image and draws a box around each one. Recognition, or classification, says what each object is. Most projects use both, and some add segmentation, which outlines the exact shape of each object for measurement.

It depends on how varied your objects and conditions are. A narrow task with fixed cameras can often start with a few hundred labelled images per class, while rare defects or many look-alike products need more. Pre-trained models and augmentation reduce the amount, and we give you a plan after reviewing samples.

We cannot promise a figure before seeing your data. We agree measures with you, such as precision and recall for each class, and report them on a test set from your own cameras that the model never saw during training. A pilot then confirms performance in live conditions.

Often, yes. Resolution, frame rate, angle and lighting decide whether existing CCTV or line cameras are good enough. We check sample footage first and recommend new cameras or lighting only where they are needed.

For low volumes or varied tasks, a vision-capable model such as GPT or Gemini can read images without training. For high volumes, real-time video or offline sites, a trained detector is faster, cheaper per image and more consistent. We often prototype with the first and move to the second.

The main drivers are the number of object classes, the data collection and labelling effort, the accuracy you need, real-time requirements, the hardware and the number of sites. You get a fixed quote after we review your requirements and sample images.

You do. On completion and final payment, the trained model weights, training code, labelled dataset and documentation we build for you are handed over, as set out in the contract, so you can retrain or move them as you choose. Third-party and open-source models stay under their own licences.

It can, if the training and test images include those conditions. We collect footage across shifts, seasons and weather, report accuracy separately for difficult conditions, and recommend infrared cameras or extra lighting where the image itself is too poor for any model to read.

Yes. Models can run on an edge device or a server on site, so detection carries on when the connection drops. Results and selected images are stored locally and sent on to your dashboard and systems when the connection returns.