8 Common Data Annotation and Labeling Tools in Autonomous Vehicle Industry
The annotation tool is the foundation of the data labeling industry. A good annotation tool is key to improve efficiency and produce high-quality training data.
The commonly used data annotation tools are as follows: 2D boxing, semantic segmentation, polygon segmentation, key points, line annotation, video annotation, 3D boxing, etc.
Data Annotation Tools
1. 2D boxing
2D boxing is rectangular, and among all the annotation tools, it is the simplest data annotation type with the lowest cost.
Common labeling objects: Vehicles, Pedestrian, Obstacles, Road signs, Signal lights, Buildings, Parking zone
2. Semantic segmentation
Semantic segmentation is a more accurate annotation type in image annotation, and it is also a time-consuming one. The annotator needs to differentiate all the content in the image.
For more information about segmentation: Semantic Segmentation, Instance Segmentation, Panoramic segmentation
Common labeling object: barrier, driving zone recognition and segmentation
3. Polygon
Compared with 2D boxing, polygon is used for accurate object detection and location in images and videos. Compared with 2D boxing, polygon is more accurate, but also more time-consuming and costly.
4. Key point
Key point is to determine the shape changes of large and small objects by multiple consecutive points, which are usually used to label touchpoints between vehicle tires and roads.
5. Polyline
Polyline is mainly used for road recognition in the automatic driving industry, defining pedestrian crosswalks, single lanes, double lanes, etc.
6. Video annotation
Video annotation is to locate and track objects in a series of images in frames. Most of them are used to train automatic driving prediction models.
What is single frame annotation?
Videos are broken into thousands of images and the target object is annotated in every single frame. Single frame annotation is always used in complex scenarios as it can guarantee quality.
What is streamed frame annotation?
At present, object tracking algorithms based on machine learning have already assisted video annotation. The annotator annotates the objects on the first frame, and then the algorithm tracks the ones in the subsequent frames. The annotator only needs to adjust the annotation when the algorithm doesn’t function well. Clients can save more money as the labor cost goes down. Streamed frame annotation is always used in simple scenarios.
7. 3D boxing
3D boxing is used to obtain spatial-visual models from 2D images and videos, measuring the relative distance between objects.
8. 3D Point Cloud
3D Point Cloud is a collaboration of numerous dots (data points) spread throughout a 3D space, where data points are collected through sensors like LiDAR. The sensors emit light and calculate the time in which it takes to be reflected back into the sensor to create each dot.
3D Point Cloud is widely used for product development and analysis in fields related to architecture, aerospace, driving, traffic, medical equipment, regular consumer items, and more.
End
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