Annotation with Polygon
Home > Labeling Services > Images
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 do not 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, For example – Cars in traffic images and landmarks in aerial images. In these cases, developers might opt for polygonal annotation. 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 confused 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 of the fastest and smartest ways of annotating the various types of objects for machine learning. In this process of image annotation, the borders of an object in the frame help to identify the object with the right shape and size with high accuracy. This type of image annotation technique is used to detect various types of objects like street signs, logos, and facial features in sports analytics.
Some Of the use Cases are:
Precise detection in Agriculture-tech
Analyzing plant health is one of the use cases under agriculture. The irregular parts of the plants are annotated to detect the plant disease at an early stage.
Annotating the logos which are in irregular shape and building a model to detect the logos.
Detect lanes, traffic, and potholes. Polygonal annotation is used to train the autonomous driving model to understand the real-world scenario.