Computer vision & perception
Develop image and video workflows for detection, classification and inspection, with evaluation tied to the real operating environment.
What must the system see reliably, and under which conditions?
Define the visual task and the decisions it supports. Consider object size, viewpoint, lighting, motion and the cost of a missed or incorrect detection. Examine whether image collection and annotation can represent those conditions.
Build a data and model workflow that makes uncertainty visible. Establish annotation guidance, evaluation splits and error categories. Compare a baseline with candidate approaches using conditions that resemble the intended application.
Connect perception to the wider system. Specify latency, confidence handling, escalation and the information operators need. Test difficult conditions and changes in the camera or environment before extending the operating scope.
What to take forward
- Image-data and annotation requirements
- Condition-specific evaluation and error analysis
- Perception integration and operating boundaries
What should I bring to a conversation?
Bring the decision you need to make, the constraints you already know and a small, non-confidential example. We can then agree whether a resource, a focused review or a more detailed discussion is the right next step.