What is Video Annotation for AI?
Polygon annotation is an essential labeling technique for supervised computer vision (CV).
Objects are labeled with polygon annotations to create a dataset, which is fed into a supervised CV model. The model learns from the annotations, enabling it to predict and classify objects when exposed to new, unseen data.
The physical environment primarily consists of complex shapes with non-linear edges – polygon annotation is considerably more effective at labeling them when compared to bounding boxes, which include a lot of useless information.
Here’s all you need to know about polygon annotation.
At its core, polygon annotation involves outlining objects in images using polygonal shapes, enabling algorithms to differentiate between objects and backgrounds.
Traditionally speaking, polygon annotations were used for image segmentation, which differs from object detection (image segmentation models seek to find the pixel boundaries between objects, whereas object detection aims to locate objects).
Lately, object detection models, such as YOLO, are able to use polygon annotation for object detection tasks. Using polygons can produce significantly better results than bounding boxes, even for object detection.
While it may appear that polygon annotation and forms of image segmentation, like semantic segmentation, are the same, polygon annotation still uses coordinates, whereas semantic segmentation labels at the pixel level.
Polygon annotation is sensible for annotating complex and non-linear shapes and objects for image segmentation or object detection tasks.
Here are the key benefits of polygon annotation:
Polygon annotation enables the precise labeling of objects within images.
By carefully drawing polygons around objects, annotators can capture the true shape and boundaries of objects with a high degree of accuracy. This results in better-quality training data for complex CV algorithms.
While many CV models will likely learn effectively when data is labeled using more straightforward bounding boxes, polygon annotation will probably produce a more accurate model when the model is intended to predict complex shapes.
Polygon annotation is versatile and adaptable to various object types and shapes.
Whether dealing with simple geometric shapes or complex, irregular boundaries, polygon annotation can be used to accurately label objects in images.
In real-world scenarios, objects are often partially occluded by other objects or shapes.
Polygon annotation allows annotators to accurately label only the visible parts of occluded objects, which helps machine learning models better understand and handle occlusion in their predictions.
Polygon annotation cuts out the noise associated with bounding boxes, which include a lot of useless or irrelevant data. This results in cleaner training data and more accurate models.
Modern labeling platforms semi-automate polygon annotation by automatically finding the boundaries between objects and shapes.
Polygon annotation plays a pivotal role in manufacturing, particularly in quality control and inspection:
Polygon annotation is invaluable in the retail industry. Read our article on why data labeling is essential in retail and ecommerce here.
Due to its abiltiy to label complex shapes, polygon annotation is instrumental in various medical imaging applications:
Polygons are essential for labeling tasks that involve complex real-world objects, Polygon annotation is used for several applications within autonomous vehicle training:
Polygon annotation is excellent for labeling complex biological forms, essential for uses in agriculture and agri-tech:
Polygon annotation is a computer vision technique that outlines objects by connecting a series of points to form a polygon.
This accurately captures the true shape and boundaries of objects, making it ideal for handling complex or irregular shapes.
Polygon annotation is commonly used in object detection, instance segmentation, and suits applications with complex shapes or occlusions, such as medical imaging or autonomous vehicles.
The team at Aya Data are trained in labeling complex images with polygons, among many other computer vision labeling techniques.
Contact us to discuss your next machine learning labeling project.
01 Comment
Candra,
09 September, 2024Hello there! This post couldn’t be written any better!
Going through this post reminds me of my previous roommate!
He always kept talking about this. I am going to send this article to him.
Pretty sure he’ll have a good read. Thanks for sharing!