Object Detection — Anchor Box VS Bounding Box

In object detection algorithms like Faster R-CNN and YOLO, anchor boxes are used to generate candidate regions and to predict bounding box adjustments and objectness scores. These predictions help refine the anchor boxes to match the ground-truth object locations. Bounding boxes, on the other hand, are the ground-truth annotations used to evaluate the model’s accuracy in localizing objects during training and testing.

Bounding Boxes:

A bounding box is a rectangular region that is used to enclose an object or a specific region of interest within an image. It is represented by four coordinates: (x_min, y_min) representing the top-left corner, and (x_max, y_max) representing the bottom-right corner of the rectangle. Bounding boxes are used to localize objects within an image and are commonly annotated in object detection datasets to provide ground-truth information about the object’s location.

Anchor Boxes:

Anchor boxes, also known as anchor priors or default boxes, are pre-defined bounding boxes with specific sizes, aspect ratios, and positions that are used as reference templates during object detection. These anchor boxes are placed at various positions across an image, often in a grid-like pattern, to capture objects of different scales and shapes. During training and inference, anchor boxes are used to predict the locations and shapes of objects relative to these reference boxes.

Key Differences:

Definition and Purpose:

• Bounding boxes represent the actual regions in an image that enclose objects of interest.
• Anchor boxes are reference bounding boxes used to predict object locations and shapes during object detection.

Annotating vs. Predicting:

• Bounding boxes are provided as annotations in object detection datasets, representing ground-truth object locations.
• Anchor boxes are used as templates for prediction during model training and inference. The model predicts adjustments to anchor boxes to match objects’ actual locations.

Static vs. Adaptive:

• Bounding boxes are static and determined by annotators based on the object’s true boundaries.
• Anchor boxes are adaptable and can be designed with different scales and aspect ratios to handle various object sizes and shapes.

Localization vs. Reference:

• Bounding boxes are used for object localization and are compared to predicted boxes to measure accuracy.
• Anchor boxes serve as reference points for predicting object locations and shapes.