Transportation
AI technologies allow to create autonomous vehicles, intelligent driving systems and help to develop roads infrastructure
AI is commonly used in the transportation system changing it in a positive way.
It learns to recognize the environment – traffic lights, signs, pedestrians, weather and road conditions, other vehicles and objects, driver's face and behaviour.
Collected and labeled images and videos train AI for further implementation in intellectual systems. Self-driving cars for example use integrated into their neural network cameras, sensors, radars and lidars to control the situation on the road.
AI and autonomous driving have already started and are going to change the world's transportation system, making it safer, easier and more organized.
It learns to recognize the environment – traffic lights, signs, pedestrians, weather and road conditions, other vehicles and objects, driver's face and behaviour.
Collected and labeled images and videos train AI for further implementation in intellectual systems. Self-driving cars for example use integrated into their neural network cameras, sensors, radars and lidars to control the situation on the road.
AI and autonomous driving have already started and are going to change the world's transportation system, making it safer, easier and more organized.
Tagias Manager helps you create a good technical task for the data or markup that needs to be performed. So that you get a training dataset that gives real results and a low error rate.
RETAIL
CASES
Use cases
Image annotation
Teaching AI to detect the signs makes driving safer and easier
It's mostly used for intelligent transportation systems, autonomous driving
Autonomous driving
Traffic sign detection
Pedestrian detection
Detecting objects on the road in order to train AI to recognize the surroundings
Safety monitoring and control, management decisions for the construction vehicles
Driver attentiveness detection
Venicle detection
Driver's face and behaviour recognition helps AI to control the situation on the road
Bounding boxes
The most common type of data annotation. Bounding boxes are mainly intended to detect objects, delineating their borders with transparent rectangles. Bounding boxes are applied in almost all industries of the data labelling market.
Classification
This method is used to determine the presence of an object in the image. A specific label is assigned to each image. Image tagging helps train AI to recognize objects' classes.
Keypoints
This type of annotation uses dots across an image to identify an object or its details/parts. Keypoints are applied mostly with facial recognition, body parts, and postures.
Polygons
Polygons are used to determine an object's shape and location with maximum precision while avoiding additional noise. Polygon annotation is applied in almost all industries.
Lines
Lines annotation is used for detecting and recognizing lanes. This type of annotation is applied mostly in the automotive industry to make self-driving cars safe.
Semantic segmentation
A data labelling method where objects are delineated in the image at the pixel level, depending on class. It's called semantic segmentation because each pixel has semantic meaning. Segmentation is usually applied in the automotive industry.