Image Annotation Services

It is a commonly used data annotation technique where an annotator draws a box around the annotation picture to resemble bounding the object of interest inside the box. The bounding box is tight and should be drawn along the edges of the target object without cutting out any parts of the object. A 2D bounding provides information about the length and width of the objects of interest. The annotation technique is used for objects that are relatively symmetrical and when the AI training data needed has little focus on the shape of the detection object.

This annotation technique involves marking the vertices of the target object with points and then joining these points to create an annotation along the object’s exact edges, thus reproducing the precise shape of the target object. It is used when annotating images where the object’s shape is important, especially for irregular objects such as vegetation and houses. With polygon annotation, a machine learning model is trained to detect, identify and differentiate between objects based on their shape variations.

3D Cuboid annotation, unlike 2D, goes further by adding the depth of an object in a given image. That creates a 3D perception of the natural world, making objects more perceptible by computer vision models. 3D cuboid annotation is useful when training computer vision algorithms to understand the depth of individual objects such as houses, cars, persons, etc.

Polyline annotation is an image annotation technique widely implemented in creating training datasets for autonomous vehicles. It involves plotting one or more continuous line segments on an image of interest. It is used with open-shape images such as roads, sidewalks, or power lines to train autonomous vehicles to identify road lanes and other surrounding features along the road. In computer vision, they are used to train drones to avoid power lines.