Autonomous vehicles
Autonomous vehicles

Bounding Box Labeling

Bounding Box Labeling

Best Image Annotation Company in Kochi & Banglore

We provide high quality data annotation and labelling services of images. Bounding box annotation is the most common and widely used model for machine tasks. The annotation will draw a bounding box over an object and label them with an object class. Generally, the bounding boxes are drawn tight with no loose end. The tasks seems simple but it requires meticulous effort to keep up the consistency. Bounding boxes are generally used for image annotation. Polygon annotation" is a multi point annotation technique to draw shapes,angles and curves unlike bounding box annoation which limits only to square or rectangle boxes. Polygon helps to annotate objects in angled photos and polygon shapes Cuboidal annotation" is drawing a cube over an object to get 3D perspective on height,width and depth. The cuboids are drawn on 2D images to get the 3d perspective.

An important image annotation technique which outlines the object in the image with a box, for object classification and localization models.

  • Traffic Light Classification: Locating all traffic signals/lights in a given image by box labelling and highlighting different attributes according to various international standards
  • Sign Recognition: Labelling all traffic and other signs in a given image and highlighting their attributes that are instrumental for navigation and control functions in autonomous driving
  • Pedestrian/Animals Classification: Labelling pedestrians and animals in a given image and highlighting their static or dynamic attributes.
  • Stereo Object Detection: Labelling all objects in a given image with the objective of classifying them as static/dynamic

Semantic Annotation

Semantic Annotation

Semantic segmentation is a pixel-level labeling which identifies all the pixels in an image and segments it into its component objects for a more meaningful representation.

  • Normal Semantic Labelling: Involves pixel-level annotation of all objects in an image across 30+ classes.
  • SVS Annotation: Surround Vision Annotation which involves labelling for the surround vision camera or for fish eye image across 40+ classes.)
Autonomous vehicles
Autonomous vehicles

Skeletal/Joint Point Labeling

Skeletal/Joint Point Labeling

Identifying individual points like facial features and joint positions in the human body

  • Vulnerable Road Users: Joint Point/skeletal labelling for all vulnerable road users in a given scenario
  • Highlighting 17 joint points with attributes in a given image

Other Annotation

Other Annotation

  • Lane Detection: Marking lanes to help identify clearly demarcated lane settings and to train computer models on vehicle perception to detect a lane
  • Headlight Assistance: Marking headlights and tail lights of all visible automobiles to help detect approaching vehicles or vehicles travelling ahead
  • Lidar Labeling: Labelling output from Lidar cameras through bounding box labelling and 3D labelling
  • Other worked cases include vehicle classification, EBA (emergency break assistance) labelling etc.
  • Labeling for green staging: Bounting box labeling for green staged images which can be applied in multiple scenarios.
  • Landmark annotation: Landmark annotation is labeling key points on an object, face or locations. It is widely used for face recognition and gesture controls. By drawing sequence of points we can determine the shape,size etc.
  • Image masking annotation: Image masking annotation implies shading some portion of the object of the image to classify. This technique can be used to simply hide unwanted content in an image.
Automation