The annotation tool is the foundation of the data labeling industry. A good annotation tool is a 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.
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 2D Boxing labeling and Cutout annotation
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, please have a look at the article: Difference Between Semantic Segmentation, Instance Segmentation, Panoramic segmentation
Common labeling object: barrier, driving zone recognition, and segmentation
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 touchpoint between vehicle tire and road.
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 of vehicle, pedestrian, rider, road.
Single Frame Annotation
Videos are broken into thousands of images and the target object is annotated in each single frame
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
Obstacles (vehicles, pedestrians) annotation within 40 meters of the emission source. 3D point cloud support object position, size, orientation, and other attributes.
ByteBridge, a human-powered data labeling tooling platform with real-time workflow management, providing flexible data training service for the machine learning industry.
1 The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
2 All work results are completely screened and inspected by the machine and human workforce.
In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
On ByteBridge’s dashboard, researchers can create the data project by themselves, upload raw data, download processed results, check ongoing labeling progress simultaneously on a pay-per-task model with clear estimated time and take control over the project status.
Progress preview: clients can monitor the labeling progress in real-time on the dashboard
Result preview: clients can get the results in real-time on the dashboard through API.
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: firstname.lastname@example.org
1 Data Annotation Service — How an Automated Data Labeling Platform Fuels Autonomous Vehicles Industry?
2 Labeling Service Case Study — Video Annotation — License Plate Recognition
3 High-Quality Training Data for Autonomous Cars in 2021
4 How Data Labeling and Annotation Services Empower Self-Driving Bus?
5 How Data Labeling and Annotation Services Empower Logistics in 2021?