Image annotation is a task of marking and outlining of objects and entities on a image and offering various
keywords to classify it which is readable for machines. This is very important task as this data helps
generate datasets which helps a computer vision models work in a real world scenario. We assure optimization
of image annotation for computer vision models in variety of functions, tools, and formats with highly
skilled workforce. Voltekon provides various image annotation services that will match your project’s needs,
Bounding Box Annotation, Polygonal Annotation, Cuboid Annotation, Road and Lane Annotation, Pixel Level
Semantic Segmentation, Point & Dot Annotation, 3D Lidar Labeling Annotation, Video Annotation and more…
Bounding Box
Bounding box annotation is the most common and widly used model for Machine learning task. The
annotators will draw a bounding box over an object and label them with and object class. Generally
the bounding boxes are drawn with no loose ends. The task seems simple but it requires meticulous
effort to keep up the consistency. Voltekon offers 2D & 3D bounding box annotation outsourcing services
for computer vision and machine learning applications. The use cases are for autonomous vehicle, SKU
classification in retail industry, food tray, House objects, and many more.
PolyLine
We Voltekon making the irregular shaped coarse objects recognizable to computer vision based machines
like self-driving cars and autonomous vehicles. We are providing a human- powered image polygon
annotation service with next level of precision. Polygon annotation Detecting the irregular shapes
or coarse objects on the road is not possible for computer vision-based machines, unless it has been
properly labeled. Polygon annotation is the right image annotation technique makes such polygonal
shaped objects recognizable to autonomous vehicles and self-driving cars to drive in the right
direction.
Cuboid
We offer is Cuboid Annotation, which annotates your two-dimensional images with projections of
cuboids enclosing objects such as cars, trucks, pedestrians, traffic cones, you name it. With some
additional information, we can turn those two-dimensional box annotations into full,
three-dimensional boxes, with height, width, depth, rotation, and relative positioning info.
Road and Lane
Road and lane annotation detecting the Lane Lines, Lane Marking on the road, Traffic Sign and
Signals , different type / section of road and Parking Area , etc…… Road Lane annotation is helpful
for autonomous vehicle to have boundary lines of a road
Pixel level Semantic Segmentation
Semantic segmentation refers to the process of linking each pixel in an image to a class label.
These labels could include a person, car, flower, piece of furniture, etc. Semantic segmentation is
image classification at a pixel level. For example, in an image that has many cars, segmentation
will label all the objects as car objects. However, a separate class of models known as instance
segmentation is able to label the separate instances where an object appears in an image.
Point and Dot
The points and dots have distinct characteristics and functions. The proper connections of points
and dots can forming desire shapes also can precise marking of required parts of an object in the
image. In point and dot annotation object in the image are labeled using points/dots to determine
shape of it and accurate marking of body parts,automotive parts, etc...
3D LiDAR
3D Lidar Labeling Annotation is Label the objects at every single point with highest accuracy. 3D
Lidar Labeling annotation is capable to detect objects up to 1 cm with 3D boxes with definite class
annotation. Used for autonomous vehicles to identify objects in the both environment indoor and
outdoor.
Video
Video Annotation is simple terms annotating the content of the video. It doesn’t mean transcribing
the video content but to annotate required parameters in a video. The Object Detection, Marking,
Labeling and Tracking are done in frame by frame.
