A Guide to 2D Annotation

The process of creating bounding boxes or polygons around specific objects in an image is known as 2D annotation. People perform it to tag specific features in the data or to identify the regions of interest. It enables AI &  ML models to easily distinguish and group various types of objects present inside two-dimensional data including images and videos. 

When 2D data annotation modules are in the development phase, they are fed with huge volumes of AI training data so that they can make better decisions when it comes to the identification of objects. If you are looking to source such data to train your 2D annotation models then reach out to us at Expert Data Annotation. We provide datasets for training such models so that they can produce accurate results.

In this blog, let us discuss 2D annotation in detail. Keep reading, and keep learning! 

What is Image Data Annotation

Understanding image data annotation is a prerequisite for gaining knowledge about 2D annotation. Systems that involve face recognition, computer vision, and more commonly use image annotation. During the training process, AI experts add captions, identifiers, and keywords to their images which makes it easy for the algorithms to understand these parameters. 

Image Classification: In this process, we categorize or label images based on their content. Using it, AI models can recognize and categorize images automatically. 

Object Recognition/Detection: In this process, we identify specific objects within an image. This feature allows AI models to highlight objects in images, videos, and even in reality.

Segmentation: This process involves dividing images into multiple segments, each with a specific area of interest. AI models that analyze images at the pixel level use this type of annotation. 

Two types of data can be annotated under image annotation:

1. 2D Annotation: It includes data from cameras, optical microscopes, or a single-lens reflex camera. 
2. 3D Annotation: It includes data from cameras, ion, electron, or scanning probe microscopes.

2D Annotation Techniques

Let us now look at some techniques used in 2D annotation in detail. 

1. Bounding Boxes

Here are some of the most widely used techniques for annotating 2D images. In this technique, annotators draw rectangular boxes around the objects of emphasis inside the image or video. Data annotators perform the process of outlining each object manually. They mark coordinates that specify the exact size and location of the bounding box. We take these bounding boxes as a reference for machine learning models to identify objects in images. For example, an image containing multiple dogs will have a separate bounding box for each dog so that the model can gain the ability to detect dogs in multiple contexts. 

2. Segmentation

During the segmentation process, we divide each image into various segments. Each divided segment represents a different object. One must note that bounding boxes just provide a rough outline of the region of an object. Segmentation on the other hand provides a more precise annotation as it marks the exact boundaries of an object. Segmentation is of further two types: 

1. Semantic segmentation: Each pixel of an image is assigned a label. This labeling classifies the pixels according to their respective classes.
2. Instance segmentation: This process distinguishes objects within the same category by their individual instances. Applications that require utmost precision use instance segmentation.

3. Polylines

Polylines are used to annotate images by drawing lines following the contours of objects. This technique is of great importance while annotating linear features in an image like roads, sidewalks, edges of objects, and more. An annotator creates such polylines by placing points along the outline of an object. These points connect further to form a continuous line. Applications that require accurate representation of boundaries, such as map creation, use the technique of polylines. This technique is also much more accurate and useful than bounding boxes. Bounding boxes fail to capture the shape of irregular or elongated objects effectively. 

After we discuss all the different 2D annotation methods, we must note that each method offers varied levels of accuracy and various applications use them accordingly. For example, researchers use bounding boxes for general object detection, apply segmentation in applications that require precise object boundaries, and employ polylines in areas that demand linear and precise annotations.

How Can Expert Data Annotation Help?

So, that was a comprehensive insight into 2D annotation. With the latest AI advancements, the same will be in high demand in the near future.

If you are just starting with 2D annotation and are not sure where to start then reach out to us at Expert Data Annotation. We have a team of domain experts who curate quality data sets for training your 2D annotation AI models. 

Further, we have an internal QC team that ensures to fix the flaws discovered during the data improvement process. We provide you with perfect AI/ML model training data sets within the promised time frame.

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