At the present, computer vision is the trendiest subfield in AI. Its uses go well beyond self-driving automobiles and drones.

From medical diagnostics to retail and financial fraud detection, computer vision has the potential to change a wide range of sectors. So, what exactly is computer vision and how does it work?

Annotation is the first step.

Training data is required for a PC to observe, respond, and understand its environment. Computer vision technologies can help with this. These kinds of tools, whether they’re called bounding boxes or polygon annotation tools, convert raw picture data into annotated images that machine learning models may use as learning data.

Self-driving cars can use computer vision tools to detect and safely avoid obstacles on the road, such as traffic cones, other vehicles, or pedestrians. However, not every annotation software is suitable for the task.

Polygon annotation

Bounding boxes are simple and inexpensive to utilize, but they struggle with unusual forms. Unfortunately, when it comes to real-life situations, irregular forms are far more prevalent.

Polygon annotation catches more lines and angles than bounding boxes (they are limited to squares and rectangles). In reality, this entails selecting precise places on the screen. Annotators who use polygon annotation can alter orientation as needed to best depict an object’s real geometry.

After mapping an item with a polygon tool, the annotator assigns an explanatory label. This tag is crucial since it informs the machine about the contents of the polygon. When a picture or video isn’t correctly labeled, it typically results in erroneous data.

Application of Polygon annotation


From buildings and staircases to trees and swimming pools, satellites and drones and must distinguish irregular objects from above.

Contours are used in aerial view imaging. Precision is essential here, as well. Polygon comes in handy in this situation.


Computer vision is enabled by polygon annotation in a variety of applications. Polygonal forms abound as well as catching them in-depth necessitates the use of the right instrument.

Polygon is a helpful technique in agriculture because it enables annotators to identify critical elements such as crop rows, insect leg locations, and other tricky details hard to catch using a bounding box.

Having the correct tools and skilled employees is critical to the quality of your training datasets when we consider polygons for computer vision. The ability to utilize comments, scanning, and rotating should all be included in polygon annotation tools.


For semantic and instance segmentation, polygon annotation is commonly employed. Irregular forms, such as humans or bicycles in a street scene, need a more accurate tool than a bounding box. Polygon annotation is a valuable tool for autonomous driving since it allows annotators to designate road edges, walkways, and obscured objects, among other things.

Precision is crucial for the correctness of your autonomous machine in the domain of data annotation. Datasets with polygon annotation are often the best approach to assure pixel-perfect fidelity.


While medical imaging, self-driving vehicles, and facial recognition may all be classified as computer vision, they are extremely distinct. It’s no surprise that each of these ML algorithms necessitates a distinct approach to data annotation.

The potential of computer vision is boundless.