Annotation with Polygon

Annotation with Polygon

The second type of image annotation is polygonal annotation, and the theory behind it is just an extension of the theory behind bounding boxes. Sometimes objects in an image don’t fit well in a bounding box due to their shape, size, or orientation within the image. As well, sometimes developers want more precise annotation for objects in an image like cars in traffic images or landmarks and buildings within aerial images. In these cases, developers might opt for polygonal annotation. The polygonal annotation is mainly used to annotate objects with irregular shapes. Unlike boxes, which can capture a lot of unnecessary objects around the target, leading to confuse training, polygons are more precise when it comes to localization.

Polygonal annotation tells a computer vision system where to look for an object using complex polygons. Object’s location and boundaries can be determined with much greater accuracy. The advantage of using polygonal annotation over bounding boxes is that it cuts out much of the noise and unnecessary pixels around the object that can potentially confuse the classifier.

Polygonal image annotation solution enables you to annotate data with precision and high quality which helps in building state-of-the-art computer vision models. With polygons, annotators draw lines by placing dots around the outer edge of the object they want to annotate. The process is like a connect the dots exercise while placing the dots at the same time. The space within the area surrounded by the dots is then annotated using a predetermined set of classes i.e. cars, bicycles, trucks.

It is one the fastest and smartest way of annotating the various types of objects for machine learning. In this process of image annotation, the borders of an object in frame with best level of accuracy that help to identify the object with right shape and size. This type of image annotation techniques is used to detect various types of objects like street sings, logos and facial features in sports analytics to more detailed recognition of such objects.

Few of the use cases are:

  • Precise detection in Agriculture-tech:
    Analyse plant health. Annotating all the irregular objects of the plants to detect the plant disease at an early stage.
  • Logo Recognition:
    Annotate the logos which are in irregular shape and build a model to detect the logos.
  • Autonomous Vehicle:
    Detect lanes, traffic and potholes. Polygonal annotation is used to train the autonomous driving model to understand the real-world scenario.
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