Image annotation is the human-powered task of annotating an image with labels. These labels are predetermined by the AI engineer and are chosen to give the computer vision model information. Depending on the project, the amount of labels on each image can vary. Some projects will require only one label to represent the content of an entire image. Other projects could require multiple objects to be tagged within a single image, each with a different label.
How Does Image Annotation Work?
To create annotated images you need three things:
- Someone to annotate the images
- A platform to annotate the images on
Most image annotation projects begin with sourcing and training annotators to perform the annotation tasks. AI is a very specialized field.
Image Annotation Services
2D and 3D Bounding Boxes
With 2D bounding boxes, annotators must draw a box around the object they want to annotate within the image. Sometimes these target objects will be the same. Other times, there may be more than one target object. Also known as cuboids, 3D bounding boxes are almost the same as 2D bounding boxes except that they also can show approximate depth of the target objects being annotated. Similar to 2D bounding box annotations, annotators draw boxes around the target objects, making sure to place anchor points at the object’s edges.
Whereas bounding boxes deal with annotating multiple objects in an image, Image classification is the process of associating an entire image with just one label.
Lines and Splines
As the title suggests, lines and splines annotation is the labeling of straight or curved lines on images. Annotators would be tasked with annotating lanes, sidewalks, power lines, and other boundary indicators. Images annotated with lines and splines are mainly used for lane and boundary recognition. As well, they are also often used for trajectory planning in drones.
From autonomous vehicles and drones to robotics in warehouses and more, lines and splines annotations are useful in a variety of use cases.
Sometimes target objects with irregular shapes can’t be easily annotated with bounding boxes or cuboids. Polygon annotation allows annotators to plot points on each vertex of the target object. This annotation method allows all of the object’s exact edges to be annotated, regardless of its shape, like bounding boxes, the pixels within the annotated edges would then be tagged with a label to describe the target object.
Bounding boxes, cuboids, and polygons all deal with the task of annotating individual objects in an image. However, semantic segmentation is the annotation of every pixel within an image. Instead of giving annotators a list of objects to be annotated, they are given a list of segment labels to divide the image.