Popular science of data annotation: ten common image annotation methods

The rapid development of computer vision is inseparable from the support of a large number of image annotation data , With all kinds of image detection 、 Commercialization of recognition algorithm , The market is becoming more and more strict about the accuracy of image annotation , At the same time, for different application scenarios , Different image annotation methods are also derived .

1、 Semantic segmentation

Semantic segmentation is based on the attributes of objects , Area division of complex and irregular pictures , And mark the corresponding attributes , To help train the image recognition model , It is often used for automatic driving 、 human-computer interaction 、 Virtual reality and other fields .

2、 Rectangular box dimension

Rectangular box labeling is also called pull box labeling , It is the most widely used image annotation method at present , In a relatively simple way 、 Convenient way in image or video data , Quickly frame the specified target object .

3、 Polygon annotation

Polygonal annotation refers to in still pictures , Use polygon boxes , Mark irregular target objects , Dimension relative to rectangular box , Polygon annotation can frame the target more accurately , At the same time, for irregular objects , It is also more targeted .

4、 Key point marking

Key point annotation refers to the manual way , Mark the key points at the specified position , For example, facial feature points 、 Human bone connection points, etc , It is often used to train facial recognition models and statistical models .

5、 Point cloud annotation

Point cloud is an important expression of 3D data , Through sensors such as lidar , It can collect all kinds of obstacles and their position coordinates , The annotator needs to classify these dense point clouds , And mark different attributes , It is often used in the field of automatic driving .

6、3D Cube dimension

Different from point cloud annotation ,3D Cube annotation or annotation based on two-dimensional plane image , The announcer frames the edges of three-dimensional objects , And then get the vanishing point , Measure the relative distance between objects .

7、2D/3D Fusion annotation

2D/3D Fusion annotation refers to the simultaneous annotation of 2D and 3D Label the image data collected by the sensor , And build relationships . This method can mark the position and size of the object in plane and three-dimensional , Help the autopilot model enhance vision and radar perception .

8、 Target tracking

Target tracking refers to moving images , Frame extraction and annotation , Mark the target object in each frame , Then describe their trajectory , Such annotations are often used to train automatic driving models and video recognition models .

9、OCR Transcribe

OCR Transcribe is to mark and transcribe the text content in the image , Help train and improve the image and text recognition model . at present , Jing Lianwen supports simplified Chinese 、 Traditional Chinese 、 English 、 Japanese 、 Korean 、 French 、 German 、 Spanish 、 Transfer of printed or handwritten pictures in more than ten languages such as Arabic .

10、 Attribute discrimination

Attribute discrimination refers to the way of manual or machine cooperation , Identify the target object in the image , And mark it with the corresponding attribute .