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

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.

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.

Intersection over Union (IoU)

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.

Claude

In this task, there are multiple objects in an image. The computer should detect objects classes and their locations.



Object Detection in Computer Vision

Dive into Deep Learning

Image classification and image localization are recognition of one object in an image.

Object detection is recognition of multi objects in an image.

Single Object Detection vs. Multi-object Detection in Computer Vision


1- Train data as we do for binary image classification.

2- Use sliding window detection.

Object Detection Steps in Computer Vision

Landmark detection is a fundamental building block of computer vision applications such as face recognition, pose recognition, emotion recognition, head recognition to put the crown on it, and many more in augmented reality.

Landmark Detection in Computer Vision

There are a few different algorithms for object detection and they can be split into two groups:

1. Algorithms based on classification. They are implemented in two stages. First, they select regions of interest in an image. Second, they classify these regions using convolutional neural networks. This solution can be slow because we have to run predictions for every selected region. A widely known example of this type of algorithm is the Region-based convolutional neural network (RCNN) and its cousins Fast-RCNN, Faster-RCNN and the latest addition to the family: Mask-RCNN. Another example is RetinaNet.

2. Algorithms based on regression – instead of selecting interesting parts of an image, they predict classes and bounding boxes for the whole image in one run of the algorithm. The two best known examples from this group are the YOLO (You Only Look Once) family algorithms and SSD (Single Shot Multibox Detector). They are commonly used for  real-time object detection as, in general, they trade a bit of accuracy for large improvements in speed.

Two Main Groups of Object Detection Algorithms

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.

Learn Before

Related

Learn After