It is often necessary to convert bounding box coordinates between the two-corner format and the center-width-height format. Computationally, bounding boxes are processed as a two-dimensional tensor of shape $$(n, 4)$$, where $$n$$ is the number of bounding boxes. To convert from the two-corner representation to the center-width-height representation, the center coordinates are calculated by finding the midpoint between the upper-left $$(x_1, y_1)$$ and lower-right $$(x_2, y_2)$$ corners, and the dimensions are determined by taking the coordinate differences: $$c_x = (x_1 + x_2) / 2$$, $$c_y = (y_1 + y_2) / 2$$, $$w = x_2 - x_1$$, and $$h = y_2 - y_1$$. The reverse conversion involves adding or subtracting half of the width and height from the center coordinates to recover the corner positions.

Bounding Box Format Conversion

In object detection, an alternative bounding box representation uses the $$(x, y)$$-axis coordinates of the bounding box's center point, along with the overall width and height of the box.

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In object detection, a bounding box is utilized to describe the precise spatial location of a target object within an image. Unlike simple image classification, object detection requires localizing the objects, which is generally achieved by defining a rectangular boundary that perfectly encapsulates the item of interest. These boundaries are plotted on an image coordinate system where the origin $$(0, 0)$$ is situated at the upper-left corner, with the positive $$x$$-axis extending horizontally to the right and the positive $$y$$-axis extending vertically downward.

Bounding Box in Object Detection

When defining locations in an image, such as bounding boxes, a specific coordinate system is used. The origin of the coordinates is located at the upper-left corner of the image. From this origin, moving to the right represents the positive direction of the $$x$$-axis, and moving downward represents the positive direction of the $$y$$-axis.

Image Coordinate System

Dive into Deep Learning

In object detection, a bounding box is often represented by the coordinates of its upper-left and lower-right corners. Specifically, the box is determined by the $$x$$ and $$y$$ coordinates of the upper-left corner and the corresponding $$x$$ and $$y$$ coordinates of the lower-right corner within the image.

Two-Corner Bounding Box Representation

Center-Width-Height Bounding Box Representation

To visually verify the accuracy of object detection models, bounding boxes can be drawn directly onto images using plotting libraries like `matplotlib`. Since plotting libraries often require specific input formats—such as the $$(x, y)$$ coordinates of the upper-left corner paired with the width and height of the rectangle—a programmatic helper function is typically used to convert the standard bounding box representation into the appropriate format for drawing the rectangular patches. This allows for a clear visual confirmation that the predicted boxes align with the main outlines of the target objects.

Visualizing Bounding Boxes

In deep learning frameworks, when working with multiple bounding boxes, they are programmatically represented as a two-dimensional tensor of shape $$(n, 4)$$. In this structural configuration, $$n$$ represents the total number of bounding boxes, while the dimension of size $$4$$ holds the numerical parameters defining each box, such as the two corner coordinates or the center position paired with width and height.

Bounding Box Tensor Shape

An anchor box (also called a prior box) is a predefined bounding box that is centered on every pixel of an input image and serves as a candidate region for detecting objects. Rather than searching for objects at arbitrary positions and sizes, an object detection model generates a fixed set of anchor boxes with varying shapes across the entire image and then predicts how each anchor should be adjusted to tightly fit nearby objects. Each anchor box is fully determined once its center position, scale, and aspect ratio are specified. Anchor boxes provide the initial spatial hypotheses that a detector refines, enabling efficient coverage of potential ground-truth bounding boxes without exhaustive search.

Anchor Box in Object Detection

In object detection, the similarity between two bounding boxes is measured using their Jaccard index, which is commonly referred to as the intersection over union (IoU). Treating the pixel area of any bounding box as a set of pixels, the IoU is calculated as the ratio of their intersection area to their union area: $$J(\mathcal{A},\mathcal{B}) = \frac{|\mathcal{A} \cap \mathcal{B}|}{|\mathcal{A} \cup \mathcal{B}|}$$. The IoU value ranges from $$0$$ to $$1$$, where $$0$$ indicates that the bounding boxes have absolutely no overlap, and $$1$$ indicates that the two bounding boxes are exactly equal.

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