The purpose of data annotation semantic is to tell the machine which things belong to which category or have certain attributes in supervised learning. Semantic segmentation annotation is actually a pixel-level classification, which predicts which object the target pixel belongs to. So the purpose of labeling is to tell the machine which pixels belong to which category.

The concept of semantic segmentation:

Semantic segmentation is a typical computer vision problem that involves taking as input some raw data (e.g., planar images) and converting them into masks with highlighted regions of interest. Many people use the term full-pixel semantic segmentation, where each pixel in an image is assigned a class ID according to the object of interest it belongs to.

Types of Semantic Segmentation:

1. Standard semantic segmentation, also known as full-pixel semantic segmentation, is the process of classifying each pixel as belonging to an object class;

2. Instance aware semantic segmentation (instance aware semantic segmentation) is a subtype of standard semantic segmentation or full pixel semantic segmentation, which classifies each pixel as belonging to an object class and an entity ID of that class.

What does semantic segmentation annotation mean?

Semantic segmentation and annotation refers to dividing complex and irregular images into regions according to the attributes of objects, and annotating the corresponding attributes to help train image recognition models. It is often used in areas such as self-driving cars, human-computer interaction, and virtual reality.

Applications of Semantic Segmentation Annotation:

1. For autonomous driving

Segment and label different areas in the picture: these classes may be pedestrians, vehicles, buildings, sky, vegetation, etc. Semantic segmentation can help SDCs (self-driving vehicles) identify drivable areas in an image.

2. For face recognition

Semantic segmentation annotation can be used for face recognition. Semantic segmentation of faces involves classification of skin, eyes, nose, mouth, hair, background, etc. Face segmentation can be used to estimate gender, expression, age, expression, and race.

3. For map image analysis

Different land types in HD maps can be segmented through semantic segmentation annotation. In addition, semantic segmentation can help automate land surveying and mapping.