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MissionLayer SystemsUtah Small Business

Capability

Computer Vision and Image Analysis

Image classification, detection, and inspection built into an application people use, rather than delivered as a model somebody else has to make usable.
Discuss a RequirementCapability Statement

The problem

Photographs, inspection imagery, submitted evidence, and scanned material accumulate faster than anyone can look at them. The usual response is a sample: review a fraction and accept the risk on the rest. A model can widen that fraction considerably, but only if it is wired into the workflow that receives the images, records the finding, and puts the uncertain ones in front of a person.

Typical technologies

  • Python
  • PyTorch
  • OpenCV
  • Vision transformers
  • ONNX Runtime
  • Object storage
  • GPU and CPU inference

What we deliver

  • Image classification against categories a program defines
  • Object detection and localisation within an image
  • Visual inspection workflows for condition, damage, defect, and anomaly detection
  • Image quality assessment, so unusable submissions are rejected at intake rather than at review
  • Automated first-pass review with a defined confidence threshold
  • Image and document matching, including verifying that a submitted photo corresponds to the record it was filed against
  • OCR combined with visual models, for material that is part image and part text
  • Image metadata extraction and normalisation
  • Human review queues for low-confidence predictions, with the model output shown alongside the image
  • Integration of all of the above into an existing application or a new one

Examples of project types

These describe the kind of work we take on. They are illustrative scopes, not descriptions of completed client projects.

  • An inspection workflow that analyses submitted photographs, classifies or detects the relevant visual condition, generates a confidence score, and sends uncertain cases to a human reviewer
  • An intake check that rejects unusable imagery at submission, with a specific reason, instead of at manual review three weeks later
  • A detection model integrated into an existing case system so findings land on the record rather than in a separate tool
  • A scoped evaluation measuring detection accuracy on a program’s own imagery before committing to a build

Expected outcome

Coverage rises from a sample to the whole set, reviewer time concentrates on the cases a model was not confident about, and every automated finding is attributable and reviewable.

Built by us

Products that show this work

Company-developed software that uses this capability — demonstrable on request, and not contract past performance.

Questions

Computer Vision and Image Analysis: common questions

What computer vision problems do you work on?
Image classification, object detection, visual inspection, OCR on images, and measurement from photographs — for example, our MissionLayer Sizing prototype measures body and foot dimensions from phone photos taken against printed reference targets.
Can computer vision run offline, on a device?
Yes. Segmentation, pose and detection models can run on a phone, a laptop, or a local server, so images never leave the site where they are taken.
How do you measure whether a vision model is accurate enough?
Against a labeled sample of the program’s own images and a threshold agreed in advance, before any larger investment. We report accuracy on real data separately from accuracy on synthetic data and say which is which.

Other capabilities

Next step

A requirement for Computer Vision and Image Analysis?

Send the solicitation number or a short description of the need and its deadline. We will tell you plainly whether we are the right fit and what a first step would look like.
Share a SolicitationRequest a Capability Discussion

Or email contact@missionlayersystems.com or call (949) 501-1291.