Data Annotation in Artificial Intelligence
The world we live in today is changing really quickly.
Everything has changed dramatically since a few years ago. Additionally, artificial intelligence is blooming and perhaps essential in this fast-paced atmosphere where businesses are attempting to succeed. Here are some reasons why Data Annotation is a crucial component of Artificial Intelligence.
The practice of categorizing and identifying data for AI applications is known as data annotation. Simply enough, annotators label what they see while separating the format they are viewing. Text, video, audio, or image can be used as the format.
Annotators identify particular things in an image and name them.
Six different types of image annotation exist:
Bounding Box Annotation: Annotators draw a square or a two-dimensional square around the chosen object.
Cuboid Annotation: Annotators describe the thing in the form of a cube, a three-dimensional square. The depth or distance of various objects can be determined using this form of annotation.
Landmark Annotation: Annotators place tiny dots around the target image to indicate their labels. This is frequently used to identify faces, such as when using face recognition to unlock a phone.
Bounding Box Annotation is a sort of annotation that is similar to the polygon annotation, however, the polygon annotation is more accurate since the annotators may pick and choose what they want to annotate rather than simply drawing a square all over the object. Using aerial photography calls for this kind of annotation. Annotators can label houses, trees, roads, street signs, and more using polygon annotation.
Semantic segmentation divides the image's items by grouping them together in various colored pixels. Annotators divide the route into three groups, for instance, to perform this annotation on an image of a road. People are pixelated in blue in the first part, cars are pixelated in red in the second, and street signs are pixelated in blue in the third (pixelated in yellow).
However, "Instance Segmentation" is a different approach to semantic segmentation. The ability of Instance Segmentation to generate a segment inside of another segment is the sole important distinction between these two segmentation techniques. This means that by designing an inner section with the names "person#1, person#2, and person#3," annotators can distinguish between the individuals pixelated in blue. Of course, the pixelated color of person #1 would differ from that of person #2, and so on.
Splines and Lines This type's function is to be aware of lane markings and borders.