Image Annotation refers to the labeling of images in the form of a single label for an entire image or using various labels for each object in an image. It is a kind of marking tool that highlights content or object in an image by sketching around it. Image annotation plays a key role in developing object detection models that are majorly used for Computer Vision projects.
Types of Image Annotation
Let’s have a look at some of the common Image Annotation types used for developing Computer Vision projects;
Bounding Box Annotation
Bounding box annotation tasks involve sketching a box around objects in an image. It mostly involves drawing a box close to the edges of objects and images are marked keeping in mind the custom requirements of data-scientists.
As one of the most commonly used image annotation types, it plays a crucial role in training self-driving vehicles by tagging objects such as pedestrians, other vehicles, cyclists and other obstacles in traffic images.
Cuboid Annotation
In the Cuboid annotation type, a box or a cuboid is sketched around objects in an image in a similar fashion as bounding boxes. Cuboid annotation depicts the depth, length, and width of the objects in addition to highlighting objects in 3D. On the other hand, bounding boxes only depict the width and length of the objects.
Cuboid annotation is mainly used for construction and building structures as it provides accurate dimensions of objects. In the field of radiation imaging, it is used for annotating medical images.
Semantic Segmentation
Semantic Segmentation also referred to as pixel-level labeling is more specific and precise. It involves labeling each pixel in an image and is different from the other types of image annotation where only the outer edges of an object are outlined. It helps to depict an image in a meaningful way by dividing it into multiple segments. Semantic Segmentation is mainly used for analyzing medical images, industrial inspection, classification of visible terrain in a satellite image and training of self-driving cars.
Line Annotation
Line Annotation is mainly used for training ML models on detecting lanes and boundaries by sketching lines on streets or roads. Annotating road lanes and sidewalks for training autonomous vehicular models to stay in a single lane without veering and to detect boundaries is the most common application of Line Annotation.
Polygonal Segmentation
Polygonal Segmentation is one of the fastest and smartest methods to annotate objects for ML. It helps to identify the boundaries of an object with the utmost precision. It also helps to accurately estimate the shape and size of objects that were captured using distant cameras. Polygonal Segmentation enables detailed detection of objects like logos, facial features, and street signs.
Landmark Annotation
Also referred to as Dot Annotation, it identifies the dissimilarities between objects and helps to count the miniature objects in images. It helps to predict the motion of pedestrians for autonomous vehicles, detect distant objects in a satellite image and identify various poses of athletes.
The above types are some of the common image annotation techniques used for training ML models.
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