Polygons are used to annotate irregular objects within an image. These are used to mark each of the vertex of the intended object and annotate its edges. Objects are not always rectangle in shape. With this idea, polygonal segmentations are another type of annotation where complex polygons are used instead of rectangles to define the shape and location of the object in a much precise way.

When it comes to Bounding Boxes in image annotation, it is very quick and easy to draw but in major cases they fall short when it comes to different kinds of uneven shapes. Bounding boxes are mostly related to rectangles and squares but when it comes to polygons it captures more lines and different/sophisticated angles. When we are simulating, it generally comes down to clicking at specific points to draw. The annotators who are working on polygons have the freedom to change direction whenever necessary to represent an object’s true shape. Once the object in the image is drawn with the help of the polygon annotation tool, the annotator tags it with a descriptive label. This Label is very necessary as it helps the machine to determine whether the labelling is accurate or inaccurate. 

Polygon Annotation and its use case:

Self-driving for Asymmetrical objects 

  • Polygon is used mostly used in instance & semantic segmentation to determine the uneven shapes such as pedestrians, bikes, cars, in a street scene. 
  • It is also a useful technique when it comes to self-driving as it allows the annotators to define the sides of a road, or sidewalk pavements, or objects which are creating a hindrance. 
  • When it comes down to the world of data annotation, we must always keep in mind the word “precision” cause without precision there will be no evolution. 
  • Also, in many ways datasets containing polygon annotation are the best way to ensure perfect pixel precision.

Drones and Satellites: Aerial Object Localization 

  • Drones and satellites must recognize uneven shapes from above that is skyscrapers, chimneys, towers. 
  • Aerial view imagery relies on outlines. Here, too, precision becomes all-important. That’s where polygon annotation comes in and the annotator has to be very precise when recognizing those outlines and drawing them accurately so that there is no inaccurate data, which would enable the machine to work smoothly. 
  • The annotators must have the resolve to get the drawings accurate, cause polygons have a very deep underlining to the fact that the ML should be able to recognize the object.  

Agriculture: Using Computer Vision to Detect Patterns 

  • Polygon annotation enables computer vision across its diverse applications. Polygonal shapes are everywhere, and capturing them in detail means using the appropriate tool. 
  • In agriculture, polygon annotation is a useful tool that allows annotators to define important features like crop rows, tracking insect leg positions, and other details that are difficult to capture with a bounding box. It also plays the role of the kind of crops, what are the pesticides that are being used to nourish the crops, how many man power are at work, keeping track of the erosion process which is very essential in maintaining the growth of the crops. 
  • When it comes to polygon annotation for computer vision, having the right tools and trained staff is essential to the quality of your training datasets. Polygon annotation tools should have features like zooming, panning, and the ability to add comments. 


The following three use cases play an active role in the world of ML where the parameters must be accurate to get the correct results which we desire. Attaining correct results doesn’t mean that there is no evolution but we always must improvise and run continuous simulations of how to make it better and also update ourselves along with the ML process. Our goals should always be to explore and conduct experiments. 

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