Image annotation is one of the most important stages in the development pictures of computer vision and image recognition applications, which involves identifying, acquiring, describing and interpreting the results of digital images or videos. Computer vision is widely used in artificial intelligence applications such as self-driving cars, medical imaging or security. Therefore, image annotation plays a vital role in the development of AI/ML in many fields.
How to label pictures?
Image annotation typically requires manual annotation by annotators who determine labels, or “markers,” and pass image-specific information to the computer vision model being trained. You can think of this process as a child asking her parents questions to explore the environment in which she lives. Parent classifies the data into common phrases such as bananas, oranges, cats, etc.
Common image annotation methods:
1. Rectangular frame labeling
Rectangular frame annotation, also known as pull frame annotation, is currently the most widely used image annotation method, which can quickly frame the specified target object in image or video data in a relatively simple and convenient way.
2. Labeling of key points pictures
Key point labeling refers to manually marking key points at specified positions, such as face feature points, human bone connection points, etc., which are often used to train facial recognition models and statistical models. Using this information the ML model learns the various parts of the face, marking specific places on the face with specific numbers, such as eyes, eyebrows, lips, forehead, etc.
3. Polygon labeling
Polygonal annotation refers to the use of polygonal frames to mark irregular target objects in static images. Compared with rectangular frame annotations, polygonal annotations can frame targets more accurately and are more targeted for irregular objects.
4. Polyline labeling
The main function of polyline labeling is to allow the computer vision system to perceive the boundaries, splines and lines of the label. ML Models in Self-Driving Cars. In , it ensures that ML models recognize objects on the road, directions, turns, and oncoming vehicles to sense the environment for safe driving.
5. Cube labeling
3D marking of vehicles in 2D pictures is mainly used to train automatic driving to judge the volume of passing or overtaking vehicles.