What are the types of image annotation?

Introduction

Image Annotation is the labelling of images, either with a single label for the entire image or with different labels for each object in an image. Each image annotation project begins with an image dataset. It is a type of marking tool that highlights content or objects in a picture by drawing a circle around them. Image annotation is important in the development of object detection models, which are commonly employed in computer vision applications.

Before any further process for image annotation it requires collection of various data and annotation process requires Image Dataset Collection.

Image Annotation Types

Let's have a look at some of the most prevalent Image Annotation types that are utilized in the development of Computer Vision projects.

Annotation of the Bounding Box

The task of bounding box annotation is drawing a box around objects in a picture. It generally entails drawing a box close to the margins of objects, and photographs are annotated to meet the needs of data scientists. As one of the most often utilized picture annotation types, it is critical in training self-driving vehicles by labelling items in traffic photographs such as pedestrians, other vehicles, bicycles, and other impediments.

Annotation of a Cuboid

A box or a cuboid is sketched around objects in an image in the Cuboid annotation type, comparable to bounding boxes. Cuboid annotation shows the depth, length, and width of the items as well as emphasizes 3D objects. Bounding boxes, on the other hand, merely show the width and length of the objects.

Cuboid annotation is primarily utilized in construction and building constructions since it offers correct item measurements. It is used to annotate medical photographs in the field of radiation imaging.

Segmentation based on semantics

Semantic segmentation, also known as pixel-level labelling, is more exact and particular. It differs from other types of image annotation in that it labels each pixel in an image, whereas the outer edges of an object are just highlighted. By breaking an image into many pieces, it is easier to describe it in a meaningful way. Semantic segmentation is primarily utilized in medical image analysis, industrial inspection, classification of visible terrain in satellite photos, and self-driving car training.

Annotation on a Line

Line Annotation is mostly used to train machine learning models for detecting lanes and limits by drawing lines on streets or roads. The most popular application of Line Annotation is for training autonomous vehicular models to stay in a single lane without swerving and to detect borders.

Segmentation of Polygons

Polygonal Segmentation is one of the quickest and smartest approaches for annotating things for machine learning. It aids in precisely identifying the limits of an object. It also aids in precisely estimating the shape and size of objects acquired by distant cameras. Polygonal Segmentation allows for the precise detection of things such as logos, facial features, and street signs.

Annotation of a Landmark

It identifies differences between items and aids in the counting of microscopic objects in photos. It is also known as Dot Annotation. It aids in the prediction of pedestrian motion for driverless vehicles, the detection of distant objects in satellite images, and the identification of various athlete stances. The picture annotation approaches listed above are some of the most commonly utilized for training ML models with Data Annotation Services.

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