What are the best common types of image annotation?

Among the many types of data labeling, image labeling is the most widely used form of labeling. For many companies that need to use image annotation for machine learning, it is very necessary to fully understand the common types of image annotation.

What are the common types of image annotation?

1. Bounding box labeling

Bounding box annotation usually achieves the purpose of collecting data by drawing a border around a specific target object in the image. When drawing the border, it should be noted that the edge of the target object should be as close as possible. This type of labeling is usually applied in the field of unmanned driving. The collected physical data such as pedestrians, obstacles, vehicles, etc. are marked through the bounding box to distinguish in real time for unmanned driving supported by artificial intelligence.

2. 3D cuboid annotation

This type of image annotation is almost the same as the image annotation method of the 2D bounding box, which draws a border around a specific object in the picture. However, since the 3D cuboid is stereo data, its annotation rules include depth, bandwidth, and length. data.

3. Polygon labeling

Since there are many target objects in the pictures that cannot be accurately labeled with bounding boxes and 3D cuboids, and due to data needs, more accurate labeling must be performed, and labelers often choose to use polygon labeling. When performing polygon labeling, the labeler will arrange multiple points on the edge of the target object, then connect the points into a line, and finally classify and label attributes according to data requirements.

4. Line and spline annotation

Although this type of image annotation method is widely used, in the actual operation process, most of them will be applied in the machine training process for identifying boundaries and lanes, usually for the recognition of driving lanes, zebra crossings, non-motorized lanes, etc. in manual driving. Data support is provided during training.

5. Semantic Segmentation and Labeling

Semantic segmentation annotation is the most specific and precise annotation method among these annotation methods. If you need more accurate data support in actual operation, or need to label more complex target objects, you will choose semantic segmentation annotation Way.


The types of image annotation are introduced here. Due to the continuous development of artificial intelligence and AI industry, image annotation work will become the core competitiveness of future business competition, which requires friends in this industry to constantly understand images Other content marked, so as to grasp more accurate business content.