Data annotation

The development of autonomous vehicle systems requires increasingly accurate and reliable perception of vulnerable road users, including cyclists. Unlike cars, cyclists' behavior is more variable and less predictable, as it depends largely on road infrastructure, environmental factors, and individual actions. Errors in detecting cyclists or misinterpreting their intentions can

CRM systems contain the core of all client knowledge, such as purchase history, manager notes, complaints, correspondence, and contract terms. The company uses this data daily, but there is a problem – it often remains unstructured and "hidden." How much time does a manager spend finding the necessary details

Panoptic scene segmentation combines semantic and instance-based segmentation, enabling simultaneous segmentation of large areas and individual objects. In the context of urban and road environments, this means the precise hierarchical marking of roadways, sidewalks, street objects such as signs, street lights, and benches. This approach provides a comprehensive understanding

To effectively train deep learning models, high-quality annotations are required that provide pixel-precise road and lane markings. Accurate lane boundary annotation enables models to correctly identify lane boundaries even in complex traffic situations — at intersections, on curved road sections, and during roadway narrowing. Correct drivable area segmentation and

For an autonomous car, the ability to recognize hidden objects is one of the most difficult skills that distinguishes a simple program from true intelligence. In the real world, we almost never see objects perfectly whole: a car in front obscures part of a pedestrian, a road sign hides behind

Labeling data for autonomous vehicles in low-light conditions and utilizing infrared radiation is a crucial step in developing safe autonomous driving systems. Night scenes present unique challenges for sensors, but traditional cameras often lose contrast and detail, and objects can remain poorly visible or distorted due to glare and