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Bounding boxes

The most common type of data annotation are bounding boxes.

These are rectangular boxes used to identify the location of an object.

It uses the coordinates of the x and y axes relative to the upper left and lower right corners of the rectangle.

The primary purpose of this type of data annotation is to detect objects and their locations.

Lines and splines

This type of data annotation is created by lines and splines to detect and recognize roads, streets, ..., which is necessary to operate an autonomous vehicle.

Semantic segmentation

This type of annotation finds its role in situations where the environmental context is a crucial factor.

It is a per-pixel annotation that assigns each pixel of the image to a class (car, truck, animal, ...).

3D cuboids

This type of data annotation looks almost like bounding boxes, but it provides additional information about the depth of the object.

Using 3D cuboids, a machine learning algorithm can be trained to provide a 3D representation of the image.

The image can also help distinguish vital features such as volume and position in a 3D environment.

For example, 3D cuboids help self-driving cars use depth information to know the distance of objects from the vehicle.

Polygon segmentation

Polygon segmentation is used to identify complex polygons to determine the shape and location of the object with the highest accuracy.

It is also one of the common types of data annotations.

Landmark and key point

These two annotations are used to create points on the image to identify the object and its shape.

Landmark and key point annotations play their part in facial recognitions, identifying body parts, postures and facial expressions.

Entity Annotation

Entity annotation is used to label unstructured sentences with relevant machine-readable information. It can be categorized into Named Entity Recognition and Intent Extraction.

saliency map