Polygon Annotation for Computer Vision: What You Need to Know

May 8, 2021

Computer vision is one of the hottest subfields of AI at the moment. Its applications extend far beyond driverless cars and unmanned drones.

Computer vision has the potential to transform diverse industries, from medical diagnostics to fraud detection in the retail and banking sectors. So, how does computer vision work?

It all starts with annotation.

For a computer to perceive, process, and respond to its surroundings, it needs training data. That’s where computer vision annotation tools come in. These tools — whether it’s a bounding box or a polygon annotation tool —transform raw image data into labeled images, known as training data, for machine learning models.

Computer vision annotation tools help self-driving cars recognize — and avoid — pedestrians, traffic cones, and other vehicles on the road. But not all annotation tools are right for the job. Let’s talk about why polygon annotation may or may not be suitable for your next computer vision project.

How Does Polygon Annotation for Computer Vision AI Image Recognition Work?

Bounding boxes are quick and easy to use — but they fall short when it comes to irregular shapes. And when we’re talking about real-world environments, irregular shapes are much more common than their regular counterparts.

Unlike bounding boxes, which are restricted to rectangles and squares, polygon annotation captures more lines and more angles. In practice, this means clicking at specific points to plot vertices. With polygon annotation, annotators have the freedom to change direction whenever necessary to best represent an object’s true shape.

Once an object is mapped with a polygon annotation tool, the annotator tags it with a descriptive label. This label is essential as it tells the machine what is found within the polygon annotation. If an image or video isn’t labeled properly, it often leads to inaccurate data.

Polygon Annotation in Action: Use Cases

Data annotation allows warehouse robots to differentiate stock and delivery drones to pinpoint addresses. So, what role does polygon annotation have to play in supporting computer vision?

  1. Autonomous Driving: Semantic Segmentation for Asymmetrical Objects

Polygon annotation is regularly used for semantic and instance segmentation. Irregular shapes—such as pedestrians or bikes in a street scene—require a more precise tool than a bounding box. Polygon annotation is a useful technique for autonomous driving as it allows annotators to define the sides of a road, sidewalks, define objects that are obstructed, and more.

In the world of data annotation, precision is critical for the accuracy of your autonomous machine. In many cases, datasets with polygon annotation are the best way to ensure pixel-perfect precision.

Polygon annotation | Keymakr

2.  Drones and Satellites: Aerial Object Localization

Drones and satellites must recognize irregular shapes from above, from rooftops and chimneys to trees and swimming pools.

Aerial view imagery relies on contours. Here, too, precision becomes all-important. That’s where polygon annotation comes in.

Object detection | Keymakr

3.  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.

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.

Polygon annotation in agriculture | Keymakr

Professional Data Annotation Services for Your Next Computer Vision Project

While self-driving cars, medical imaging, and face recognition may all fall under the same category of computer vision, the reality is, they’re quite different. It’s not surprising that each of these machine learning models require different data annotation techniques.

Computer vision has limitless potential. But the performance of your AI hinges on its training data. Make sure your training datasets are up to par by partnering with a professional data annotation company that specializes in machine learning.

Keymakr offers affordable image- and video-based training datasets created by our team of expert data annotators.

Are you looking to improve the quality of the datasets you’re using to train your algorithms? Get in touch with a member of our team to schedule your personalized demo today.

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