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What is best methods are there for image data annotation?

Image data annotation is the process of selecting objects in an image and labeling them by name, which has a wide range of segmentation applications. Especially in the automatic driving scene, it can accurately identify pedestrians, lane lines and traffic signs in the image. So, what methods are there for image data annotation?

data annotation with gwap3 l

What methods are there for image data annotation?

Image data labeling is to find the items and parts that need to be marked on the picture to frame or mark the mark points according to the task requirements. For example, for a picture of parking on the side of the road, some tasks only require frame selection of the car body to let AI know that the marked part is a car.

1. Bounding box labeling

Bounding box is the most common and widely used image data labeling method, also known as drawing box labeling, which is the process of fitting a tight rectangle around the target object.

2. Area labeling image

Generally speaking, image regions are obtained based on image segmentation. For example, in autonomous driving scenarios, drivable road surfaces are recognized and marked and divided, and corresponding attribute labels are marked to help machines train image recognition models.

3. RBI

Also known as key point labeling, the elements that need to be marked (such as faces, limbs, etc.) are marked according to the required positions, so as to realize the identification of key points in specific parts.

4. Point cloud drawing frame

In the data labeling software, a 3D model is generated for the image, and the 3D frame drawing of the outer contour of the labeling object is the same as the 2D frame drawing. It is also necessary to add specific labels to the three-dimensional frame, so as to realize the recognition of the sense of space in machine training.

5. 2D/3D fusion labeling

Annotate the data groups mapped between 2D planar images and 3D point cloud images, and support multiple functions such as automatic edge-fitting, cross-frame copying, distance measurement, and separation of 2/3D image annotations.

6. Target tracking

Target tracking refers to the extraction of frame annotations in dynamic images such as videos and images, and the precise and accurate annotation of the target object data in each frame of the picture, and then describe their trajectory. This type of annotation is often used to train autonomous driving. model and urban security video recognition model.

data annotation with amt2 l

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