To help automobile OEMs and self-driving car companies accelerate the development of perception algorithms for autonomous vehicles, Scale API has launched the Sensor Fusion Annotation API for LIDAR (Light Detection and Ranging) and RADAR (Radio Detection and Ranging) point cloud data.
The combination of human and artificial intelligence results in rigorously tested training data that help autonomous vehicles more quickly learn to navigate independently while accurately identifying road markers, vehicles, and other objects in an instant.
The company utilizes machine learning, statistical models and human-generated data to deliver object recognition to accurately analyze millions of camera images, LIDAR frames, and RADAR data each month.
Scale API already offers an Image Annotation API which uses object recognition to label datasets for training computer vision models and algorithms. In addition, Scale API provides APIs for OCR and image transcription, categorization, comparison, and data collection.
Many automobile OEMs and self-driving car companies are already using Scale API’s Image Annotation APIs to produce premium training datasets for their computer vision algorithms.
According to the firm, developers can use Sensor Fusion and Image Annotation APIs for:
- LIDAR/RADAR Annotation: Identifies objects in a 3D point cloud and draws bounding cuboids around the specified objects, returning the positions and sizes of these boxes.
- Semantic Segmentation: Classifies every pixel of an image according to the labels provided to return a full semantic, pixel-wise, and dense segmentation of the image.
- Polygon Annotation: Identifies objects (such as vehicles, pedestrians, cyclists, and more) and draws bounding polygons around the specified objects, returning the vertices of these polygons.
- Bounding Box Annotation: Identifies objects and draws bounding 2D boxes around the specified objects, returning the vertices of these boxes.
- Line Annotation: Identifies the different features of a road, such as lane lines, and draws segmented lines along each object, returning the vertices of these segmented lines.
- Point Annotation: Identifies the location of objects and draws points at specified locations, returning the locations of these points.
- Cuboid Annotation: Identifies objects and draws perspective 3D cuboids around the specified objects in camera images, returning the positions and sizes of these boxes.