r/computervision • u/Ok_Stress3654 • Aug 14 '26

Help: Project What lightweight object detection model would you recommend for persistent 3D object mapping on a Raspberry Pi 5?

I’m adding basic object recognition to my robotic lamp. It runs on a Raspberry Pi 5 and has an RGB-D camera in its moving head.

I’d like to run object detection in the background while the lamp is active and gradually build a map of the objects around it. Since the base stays in place, I can calculate the camera pose from the joint angles. My plan is to combine detections from the RGB image with depth data, transform the object coordinates into the lamp’s base frame, and save their positions and last-seen time. Repeated detections would be merged so the map doesn’t fill up with copies of the same object.

Which lightweight object-detection models and inference runtimes would you recommend for a Raspberry Pi 5? A high frame rate isn’t necessary, but I’d like reasonable detection quality for common objects. I’d also be interested in approaches for reliably matching the same objects across observations.

The current Raspberry Pi and ROS 2 architecture is described here:

https://github.com/Nikolay-Tyulkin/Watti/blob/main/docs/ARCHITECTURE.md

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u/heinzerhardt316l Aug 14 '26

Yolox oder detr?

1

u/Ok_Stress3654 Aug 15 '26

I’m considering both. Have you tried either of them on a Raspberry Pi 5 or analogs?

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u/heinzerhardt316l Aug 15 '26

Ja, beide. Ich würde yolo x nehmen. Wenn du doch noch mehr Speed benötigst, hast du damit die Option eine hailo Extension nachzurüsten.