Automated labelling of objects as the basis of Big Data

Automated labelling of objects as the basis of Big Data

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The problem

For the development of new autonomous driving functions, several million objects suddenly have to be labelled by hand in high quality within a short time

In the automotive industry, the demands on Big Data are increasing due to the introduction of semi-autonomous driver assistance systems. As very large amounts of reference data are required for the next generation of autonomous driving functions, automated algorithms that allow to minimize the time required and to keep the costs within a real framework have to be sought.

The solution

A cloud-based software that performs automatic object annotation using deep learning

After the manual labeling of each individual object, an automatic object annotation is performed. This means that with the help of the software, objects known to the algorithm do not have to be manually labelled repeatedly. The software is operated in the cloud in order to efficiently access the large amounts of data. In addition, the easy accessibility in the cloud facilitates project coordination and the processing of large data sets.

Before

  • All labels had to be labeled and controlled manually
  • Bounding boxes must be set manually, for image sequences interpolation is possible. Afterwards a quality control is carried out

Afterwards

  • Object tracking and interpolation algorithms support manual labeling and accelerate the manual annotation process. 
  • In automatic recognition, objects are recognized and annotated fully automatically. Only the quality inspection has to be done
  • The cloud-based deployment of the tool allows customers to store data in the cloud and access it from anywhere.
  • Objects to be annotated that are already trained in the deep learning network can be automatically recognized.

Insights

What had the customer tried before?

The customer tried to overcome this challenge by splitting the large amount of data and manual labeling via different providers abroad. 

Additional challenges at the customer

  • Different driving scenarios with different degrees of complexity (motorway vs. urban)
  • Many different object classes to be detected, which hardly differ from each other
  • Data management and permanent storage of huge amounts of data

What criteria were important to him?

  • temporal flexibility
  • reasonable hourly rates
  • constant high quality

Business

Benefits

  • A scalable deployment concept allows the tool to be easily rolled out and deployed.
  • By managing data in the cloud, the data can be efficiently managed centrally and viewed from anywhere after uploading.
  • By training the neural networks specifically for the customer, added value can be created in customer-specific use cases.
  • Automatic recognition of customer-specific object classes.
  • Time and cost savings through efficient and automatic labeling.

The project schedule

  • The customer presents the scenario and the data to be annotated and explains which objects are to be recognized in which form. This is best done with a sample of data or exemplary data.
  • The customer then receives an assessment of which automations are possible. An estimation of the amount of data required provides a reference as to how much he has to label manually before training his deep learning network is possible.
  • The customer receives an instance of the tool in a cloud environment.
  • Quality checks by a workforce confirm the correctness of the annotations before delivery.

Project maturity level

POC (Pilot)

Project duration

The application and cloud infrastructure can be deployed within a few days. The connection of customer-specific cloud infrastructure depends on the customer infrastructure. The enabling of the neural networks for automatic detection can be managed by the provider and carried out for the customer and depends on the application.

Project cost
(digits)

6

Running costs
(per month, digits)

4

Technical

Skills required by the customer

  • Fundamental IT know-how for ordering and setup
  • Short introduction for the use of the tool
  • Training experience for neural networks is advantageous - otherwise this is provided as a service by the expert

The project schedule

  • After determining the scenario to be annotated, an instance in the cloud and the specific application was set up for the customer.
  • The data was loaded into the cloud. The customer manually annotated a portion of the data.
  • The network is trained and provided.
  • Pre-annotation of the data.
  • A quality check of the annotations is carried out, if necessary, further data is labelled and trained. This step is repeated until a satisfactory quality in object recognition is achieved.
  • Delivery/use of the annotations.


Technical Facts

  • Pilot phase
  • Cloudbased application with docker-based deployment
  • Deep Neural Networks for object detection
  • Computer vision algorithms for object tracking
  • Application is accessible to the user via common browsers (Google Chrome, Firefox)
  • Can also be deployed in the customer's own cloud


Integration/APIs


Connection of Azure BLOB Storage Containers to the application. 
Connection of other cloud storage possible. 


Where is the data stored?


The data is stored in the cloud storage. The cloud storage is currently located in the dedicated Bertrandt Azure Cloud. However, it is also possible to connect the customer's own cloud storage. 

Providers

Torsten Bohnet (Product Manager)

"If a customer is looking for locally running annotation software for processing small amounts of data and automatic recognition of very diverse object classes, we can't help, but when it comes to rolling out software for annotating large amounts of data with frequently recurring object classes in an automotive environment and large project teams, we are at the forefront".

Torsten Bohnet

The information may of course vary in individual cases. Please contact the provider for an assessment of your project.