Video Annotation Supports Robotics AI Development

Sep 30, 2021

Annotated video training data plays an important role in the development of computer vision based AI models for robotics applications. By labeling each frame of video training data annotators create vital contextual information that allows AI systems to function effectively on busy production lines and in crowded warehouses.

Video annotation specialists, like Keymakr, can help robotics AI developers to navigate the complex and time consuming process of video annotation, and support the refinement of promising AI applications.

This blog will identify some of the exciting use cases emerging for AI in the field of robotics. We will then focus on some of the specific issues affecting video annotation for AI companies and show how video annotation services can help.

Video annotation | Keymakr

Promising robotics AI applications

Computer vision based models can lead to more efficiency and safety in industrial production. By protecting human workers from dangerous tasks and spotting potential defects AI can improve a wide range of processes:

  • Object detection: Reliable object detection is essential for AI powered machines operating on busy production lines. Annotated video data allows AI models to recognize a range of objects in a production context.
    Video data also incorporates the dynamic movement of factory settings. With this training data it is possible for robots to identify target objects and manipulate them as required.  
  • Environmental sensing: Warehouses and production lines can be chaotic and dangerous places. With machinery, production items and humans moving around AI powered machines it is essential that automated systems are aware of their surroundings and able to stop and move safely. Annotated video data teaches robots how to plan their movements and avoid collisions.
  • Quality control: Assessing product quality is an essential part of the production process. Defects in parts or final products can lead to customer dissatisfaction and significant costs. Whilst human inspection based quality control is often effective, at times smaller issues can be missed.
    AI models, backed by video annotation, can identify defects that may be invisible to the human eye. They can also work quickly and constantly, increasing operational efficiency.
  • Waste management: Video annotation is also helping to create computer vision based AI models that are capable of sorting waste. Robots powered by this technology can identify waste types and manage them accordingly. Automating this process helps workers avoid potentially dangerous waste products, and can increase waste management efficiency overall.

Video annotation for robotics AI

For robotics AIs to flourish it is important that developers have access to accurate, high quality video annotation. However, it can be difficult for AI companies to establish functional video annotation operations. Annotating thousands of frames per piece of training footage represents a large labour and management responsibility. As a result many AI pioneers in this sector choose to outsource video annotation to providers with expertise.

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Video annotation services

Keymakr is able to provide exceptional video annotation services to robotics AI innovators. Key advantages include:

  • Object interpolation: Object interpolation accelerates video annotation by automating object tagging between specific frames. Annotators select an object in one frame, and then again further on in the piece of training footage. The annotation platform then locates the targeted object in each intervening frame.
  • Task management: Keymakr’s annotation platform allows multiple annotators to work on the same piece of video at the same time, and these annotations can be seamlessly integrated. This helps to improve annotation efficiency and quality.
  • Data creation: It can sometimes be hard for developers to find the video data that they need. Keymakr’s in-house video creation facilities can help robotics AI companies to develop highly specialised, unique training datasets.

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