Computer Vision development services

For detection tasks a few hundred well-labelled images per class is often enough for a first model thanks to pre-trained backbones; rare defect types need targeted collection. We start with what you have, run a baseline and tell you where more data is worth gathering. Synthetic augmentation (lighting, rotation, noise) stretches small datasets, but it never fully replaces real examples of the failure modes you care about.

Usually yes. Most IP cameras can stream to an edge box or server where the models run. We assess resolution, angle and lighting during feasibility and recommend adjustments only when they materially improve accuracy. For older analogue systems an inexpensive encoder gets the feed onto the network; we have yet to find a camera setup that could not be worked with.

Edge devices such as NVIDIA Jetson suit real-time inspection and sites with poor connectivity or privacy constraints; cloud suits batch processing and central analytics. Many deployments do inference at the edge and send events and thumbnails to the cloud. We often start in the cloud for speed of iteration and move to edge once the model and thresholds are stable and the hardware case is proven.

We design for the applicable law and your policy: consent and purpose limitation, on-device processing where possible, blurring of non-subjects, retention limits and access logs. Face recognition is only built where there is a lawful, documented basis. For most operational use cases (counting, safety, queue analytics) identity is not needed at all, and we design so it is never captured.

It depends on the task, image quality and how the classes are defined. We report precision and recall on your data from a baseline early in the project and set targets together, rather than quoting generic figures. As a rule, well-lit fixed-camera tasks with clear class definitions reach very high accuracy; ambiguous defects and variable outdoor scenes take more data and iteration.

Lighting, new products or camera moves can degrade accuracy. We monitor confidence and sample predictions, alert on drift and retrain on new examples; the retraining pipeline is part of the delivery. Your operators can flag wrong predictions from the interface, and those examples feed the next training round, so accuracy improves with use rather than decaying.