What Is A Bounding Box In Computer Vision?

Bounding boxes are a fundamental concept in computer vision, serving as rectangles drawn around regions of interest in images to identify specific objects. They play a crucial role in two main scenarios: during the data labeling process, where they are drawn around objects to train vision models, and during inference, where the models return bounding box coordinates that indicate the location of identified objects. Labeling tools, such as the one offered by Roboflow, assist in creating precise bounding boxes that should tightly encompass objects to minimize background noise. Despite their rectangular shape, which may include some background, bounding boxes can overlap to accommodate multiple objects, such as identifying both a shipping container and its ID. Vision models like YOLOv10 and RT-DETR provide output in various coordinate formats, such as xyxy or xywh, which can be visualized to assess model performance. Tools like the Python library supervision can be used to draw these predictions, helping users to visualize and refine their models effectively.