Model training and data pipelines
Vision-based surface defect detection
Inspecting road surfaces for defects by eye is slow and inconsistent. The goal was to explore detecting defects automatically from images captured in the field.
Computer Vision & Applied AI
Computer vision can turn images into structured information: whether a defect is present, where an object is, or what condition a surface is in. A useful result depends as much on the data and the surrounding software as on the model. TurnipSoftware Inc. brings hands-on model-training experience together with the engineering needed to put image analysis to work.
- CaptureImages collected in the field or on site
- PrepareOrganize, label, and check the data
- Train and evaluateIterate on representative images
- Integrate and reviewResults reach people and systems
Model training
Training a vision model involves selecting a suitable approach, preparing labelled examples, and iterating on training until the model behaves well on images it has not seen before.
- Training of computer vision and neural-network models.
- Selection of model approaches suited to the problem and the available data.
- Evaluation against representative images from the real environment.
Image-based detection and analysis
Detection and analysis use cases range from finding defects on a surface to recognizing items or conditions in a scene. Defining exactly what should be detected, and what counts as a correct answer, is the first step.
- Defect and anomaly detection in images.
- Object detection and classification.
- Definition of detection criteria together with the people who use the results.
Data collection and preparation
Most of the effort in a vision project goes into the data. Images must be captured consistently, organized, labelled, and connected to the context in which they were taken.
- Data-collection workflows for images captured in the field or on site.
- Organizing, labelling, and preparing training data.
- Pipelines that move captured data from collection through to training.
Integrating AI into applications and operations
A model only creates value when its results reach the people and systems that act on them. That means integration with applications, clear presentation of results, and a way to review and correct them.
- Integration of model output into business applications and workflows.
- Review steps where people confirm or correct automated results.
- Monitoring of how a model behaves once it is in use.
Assessment and prototyping
Not every problem is a good fit for AI. A short assessment and a focused prototype can show whether an approach is practical with the data you have, before larger investment.
- Assessment of candidate AI use cases against available data and constraints.
- Prototypes that test feasibility on real examples.
- Clear recommendations, including when a conventional approach is the better choice.
Related experience
Software development
Industrial automation and inspection software
Industrial equipment needed software that could control motion, interface with hardware, run optical inspection, and present clear workflows to the people operating it.
Start a conversation about Computer Vision & Applied AI
Contact TurnipSoftware Inc. to discuss a retail system, software integration, modernization project, or applied AI opportunity. A short description of your current situation and desired outcome is a useful starting point.