With the continuous image development of artificial intelligence in recent years, the algorithm of its core competitiveness has attracted people’s attention and has been applied to the work of all walks of life. By strengthening the use of artificial intelligence core algorithms, it can provide more thoughtful services for people’s work and life. In this process, the most familiar work is image annotation. This emerging project has made many people full of curiosity about it. What exactly does image annotation do? What types are included? Next, let us find out together.
1. What is image annotation
Simply put, image annotation is a process of adding relevant tags to images through technical means. After image annotation, it can provide the most basic data for artificial intelligence in many fields, so as to deepen the learning and realization functions of artificial intelligence, and provide people with more comprehensive and accurate services through such operations.
2. Types of Image Annotation
1. 2D bounding box annotation
To put it simply, 2D bounding box annotation is to mark the drawing frame around the target object in the target image, and when drawing the border, draw at the edge of the target object, and then provide the related software with the marked image .
2. 3D three-dimensional frame labeling
The 3D stereo frame annotation, which is quite similar to the 2D bounding box, is to draw a frame around the target object in the stereo image. Its annotation data includes depth, width and length.
3. Polygon labeling
Polygon annotation is usually applied to target objects in stereo images, such as buildings in aerial images or cars in traffic images, etc., and the drawn borders will be more accurate.
4. Line and spline annotation
This type of labeling is mainly aimed at the data storage of the driving training system, and provides boundary data support for the system by labeling sidewalks and roadways. In addition, line and spline labeling can also be applied to the item placement training system of the storage robot.
5. Semantic Segmentation and Labeling
The accuracy of semantic segmentation annotation is the most accurate, and its drawing of target objects can be accurate to the level of pixels. Compared with the above annotation types, the semantic segmentation data will be more comprehensive and accurate, and the data types will be more abundant.
In short, image annotation provides very favorable data support for all walks of life to carry out artificial intelligence business. Familiar with and master these contents, and constantly improve in continuous practical application, can help everyone to open up new business scenarios.