OpenMV Embedded Vision: The EEI #57 Interview With Kwabena Agyeman

OpenMV embedded vision is one of the more practical takes on “edge AI” you can watch right now: Our recent full recording of Elektor Engineering Insights #57 is up on YouTube, featuring OpenMV President and Co-Founder Kwabena Agyeman and a no-hype discussion of what it takes to ship vision features on microcontrollers and similarly constrained targets.

Watch the full interview here:

OpenMV Embedded Vision As “Vision-as-a-Sensor”

A lot of computer vision coverage still assumes a Linux SBC (or a GPU) is the default starting point. OpenMV flips that: treat the camera as a first-class embedded peripheral, and build the workflow so you can iterate quickly without turning your project into a fragile science fair demo. That mindset matters if you’re trying to do things like barcode/AprilTag reads, blob detection, simple tracking, inspection, or lightweight classification in products that have real power budgets and real BOM limits.
 

In the conversation, the emphasis is on engineering trade-offs: where microcontroller-based vision wins, where it stops being sensible, and which parts of a “vision pipeline” you should sweat first (sensor choice, exposure/lighting, resolution/frame rate, memory pressure, and integration with the rest of the system). If you’ve ever had a model that “works on the bench” and then collapses when you change a lens, light, or enclosure, this is the kind of grounding that saves time.
 

What Engineers Will Get Out of EEI #57

The episode is useful even if you never buy an OpenMV board, because the principles apply broadly: Keep the pipeline measurable, keep iteration tight, and be honest about compute and memory ceilings. There’s also a practical angle on tooling and debugging, including why a tight edit-run-test loop is often more valuable than chasing bigger models. And yes, the “edge AI” part gets treated with the right level of skepticism: what’s deployable today, what’s marketing, and what actually ships.