About the Client
Client is known for providing commendable training data sets that are quite accurate. They opulent data sets are acknowledged for producing the greatest possible machine learning results in the development of autonomous vehicles. With a wonderful vision of technological advancement, they are trying to create futuristic data annotation services for all the clients.
Challenges
- The client required 3D data annotation tools compatible with their proprietary flash lidar technology, along with robust project management and a secure workforce. This included object detection support using 3D cuboid tools and lane marking annotations to determine drivable areas using 3D point cloud segmentation.
- The file size of LiDAR generated data is so huge that it needed high configuration devices to store, manage, and utilize such data with a more complex user interface.
- 150+ skilled people were required to be hired and trained in a week’s time.
Solution
- Annotator training/qualification : Our secure and highly effective training and qualification program helped annotators execute unique and complex 3D annotation requirements while maintaining high accuracy levels.
- Robust quality assurance : Our platform’s built-in heuristic check options, flexible scoring rubrics, multi-user verification workflow and quality analytics facilitated accurate and high quality annotations.
- End-to-end project management : Our experienced project managers handled all aspects of the project including data preprocessing, workforce planning/management, quality assurance and on-time deliveries.
- High-precision 3D labelling tools : Our robust 3D cuboid and segmentation tools supported class detection for objects like moving vehicles, pedestrians and drivable areas by focusing on retro refractor points.
Result
- 1.5M Lidar annotations processed, analyzed and delivered.
- 200+ Full-time data annotators completed the tasks in 90 days.
- 99% High-quality annotations at recall and precision rate for 3D object detection and segmentation projects.