Annotations
The project is developing an original method of providing annotations that is being supported by artificial intelligence, data fusion, automatic detection and tracking of objects, and active learning algorithms. Data in the DARTS-PL system will contain various 2D and 3D annotations of objects, including: vehicles, pedestrians, two-wheeled vehicles, road signs, traffic lights and obstacles.
Automated data annotation
Annotation is a technique for recognizing and labeling each object in a dataset, i.e. a video frame. This data is crucial for training AI models to detect objects, understand the road environment, and make real-time driving decisions.
Automated data annotation is essential for developing autonomous vehicles as it speeds up the process of labeling large amounts of sensor data such as images, LiDAR data, and radar data.
Traditional manual annotation is time-consuming and prone to human error. Automated annotation tools, powered by machine learning algorithms, streamline this process by automatically identifying and labeling objects such as pedestrians, vehicles, road signs, and lane markings. By reducing human involvement, automated annotation not only speeds up data processing, but also improves accuracy and scalability, enabling faster iteration and development cycles.
Automated data annotation is essential for developing autonomous vehicles as it speeds up the process of labeling large amounts of sensor data such as images, LiDAR data, and radar data.
Traditional manual annotation is time-consuming and prone to human error. Automated annotation tools, powered by machine learning algorithms, streamline this process by automatically identifying and labeling objects such as pedestrians, vehicles, road signs, and lane markings. By reducing human involvement, automated annotation not only speeds up data processing, but also improves accuracy and scalability, enabling faster iteration and development cycles.
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