How AI Object Detection Works in a Dashcam App

From camera image to boxes

A road-object detector processes an image and predicts supported object classes, bounding boxes and confidence values. In AyniCam the camera AI runs on the iPhone. A displayed label is the model’s interpretation of the available image, not a complete account of everything around the car.

What AyniCam can label

The detector family supports people, bicycles, motorcycles, cars, buses, traffic lights and stop signs. An object outside these classes can be missed or mislabeled. A car box does not identify its exact model or guarantee its front/rear orientation. Confidence is not a collision probability or a measured distance.

One camera means limited geometry

Approximate placement can support a simplified view, but the camera does not supply a surveyed scene. Occlusion, camera angle and changing appearance affect the estimate. Virtual View simplifies camera observations; 3D Maps adds roads and buildings from mapping data. These are distinct information sources.

Masks add a different output

The Pro segmentation model predicts individual object masks and a road-surface mask on a dedicated page. Masks are useful for seeing an estimated shape rather than only a rectangle. A road mask is not lane detection, a crosswalk detector or a guarantee of drivable space.

How to assess a result

While stationary, compare the output with the source image and note missed objects, wrong labels and instability. Keep device, scene, model and settings recorded for any comparison. Glare, darkness, rain and occlusion can reduce useful observations. Picker animations and simulator detections are illustrations; no measured accuracy, latency or universal Neural Engine execution is established by them.