Perception

Object Detection

Object detection is the computer vision task of localizing and classifying objects in an image, producing bounding boxes or instance masks with category labels. Modern detectors span two-stage models like Faster R-CNN, single-stage models like the YOLO family, and transformer-based approaches like DETR, with open-vocabulary variants such as OWL-ViT and Grounding DINO detecting categories described in free text.

Why it matters for physical AI

Detecting and localizing task-relevant objects is the entry point of most manipulation pipelines, and open-vocabulary detectors let robots ground language instructions in what they see.