Data Annotation, an “Engine” for Self-Driving Car
With the development of computer vision technology and the increasing intelligence of the travel eco-system, the typical application scenario is autonomous driving.
Self-driving cars are really coming
In 2018, the world’s first driverless taxi was on the roads. This is the first driverless taxi ride in Frisco, Texas, by Silicon Valley start-up Drive.ai.
In China, Baidu is the leader in the auto autonomous driving industry. On 30 Nov 2019, Baidu launched a trial operation of RoboTaxi in Guangzhou, the second biggest city in China.
Technical support behind self-driving cars
In the process of autonomous driving, the car itself needs to have a number of “skills” such as perception, planning, decision-making, and control, which can be collectively referred to as “artificial intelligence”.
However, the algorithm of the car itself can’t handle more and more complex scenes without massive real road data.
The data annotation is supposed to make machines understand the world. In auto autonomous driving, the annotation scenarios usually include changing lanes to overtake cars, passing intersections, unprotected left turns and right turns without traffic light control, and some complex long-tail scenarios such as vehicles running red lights, pedestrians crossing the road, vehicles parked illegally on the roadside, and so on.
Several data annotation tools commonly used in auto autonomous driving
Data annotation usually deals with speech, text, image, and video. The annotation types include classification, picture frame, annotation, etc. In the field of self-driving, the annotation tools commonly used include 2D boxing, 3D cube, lane line, polygon, semantic segmentation,3D point, and so on.
Only backed up by high-quality training data, can self-driving cars navigate at high speed.
High-quality data is the future of the industry
As self-driving cars move from the laboratory to reality, the safety of self-driving cars has drawn more and more attention in public.
As the basis of automatic driving, the quality of labeled data directly affects the self-driving final model. Scalable, high-quality, and refined data can greatly improve safety and practicability and contribute definitely to the landing process.
In fact, the high-quality requirements for training data in the auto autonomous driving field also outline the future development of the data labeling industry. Different from the tags of “advanced” and “high-tech” in the artificial intelligence industry, data labeling is still a labor-intensive industry, which has been in an extended state for a long time.
With the implementation of AI projects represented by auto autonomous driving, more and more AI enterprises realize that high-quality data sets are the key to the success.
In the future, refinement, scenario-based, and customization will be three important directions of the data labeling industry. The high-quality labeling data will support the future of the artificial intelligence industry.
Bytebridge, a human-powered data training platform, provides high-quality services to collect and annotate different types of data such as text, image, audio, and video to accelerate the development of the machine learning industry.
- Dealing with complex tasks, the task is automatically transformed into tiny components to minimize human errors
- 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
- All work results are completely screened and inspected by machines and the human workforce
In this way, ByteBridge can affirm our 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.
Thomas C. Redman sums up the current data quality challenge in this way: “Increasingly complex problems demand not just more data, but more diverse, comprehensive data. And with this comes more quality problems.”
Bytebridge is dedicated to empowering the machine learning revolution with no bias training data.
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: email@example.com
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