"Real-time detection" sounds intimidating, but the modern tooling makes it surprisingly approachable. With YOLO for the model and OpenCV for the video pipeline, you can go from a webcam feed to live bounding boxes in an afternoon. Here's the path I follow.

1. Pick the Right YOLO Model

YOLO ships in sizes from nano to extra-large. Start with the smallest model that hits your accuracy bar — a nano or small variant often runs in real time even on modest hardware, while the larger ones need a GPU. Match the model to where it will actually run.

2. Build the OpenCV Pipeline

OpenCV handles the unglamorous but essential parts: reading frames from a camera or video, resizing them to the model's input size, and drawing boxes and labels back onto the frame. Keep this loop tight — every millisecond per frame counts toward your FPS.

• Capture → preprocess → infer → draw → display
• Resize once, reuse buffers, avoid per-frame allocations
• Filter detections by a confidence threshold

3. Keep It Fast

If FPS drops, the usual fixes are: use a smaller model, lower the input resolution, batch frames, or move inference to a GPU. Skipping every other frame and interpolating is a cheap trick when perfect smoothness isn't critical.

4. Deploy Behind a Web API

To make detection useful in a product, wrap it in an API. A FastAPI endpoint can accept an image or video stream, run inference, and return detections as JSON — so any frontend or service can consume it without touching the model directly.

Wrapping Up

Real-time CV is mostly about a clean loop and the right model size. Get those two right and the rest follows. Have a detection use case in mind? Let's build it.