We now live in a world where a driverless car can stop at a red light, you can unlock your phone with your face, and illnesses can be detected through medical images. Have you ever wondered how computers process what they see?
Computer vision is a subset of artificial intelligence (AI) that enables computers and systems to understand visual inputs like images and videos. Image annotation is the human-powered task of labeling images to train computers in recognizing visual data on their own.
The learning process is similar to how we teach children to identify objects like a ball and a block. We associate a ball as a round object and a block as a square. As they get familiar with the difference, they determine which objects are round and are square. This is the same way we teach computers—we feed them data that teaches them to categorize. To effectively train them, a huge amount of these labels are required to be annotated and validated manually by humans.
In order to choose the correct image annotation tool for your use case, you first need to understand the different annotation types. Let’s take a closer look at how image annotation services are used.
Bounding Box Annotation is the most well-known type of image annotation service. Drawing rectangles around objects may seem simple, but these rectangular frames are used to determine target object position with x and y coordinates. Bounding boxes help models locate and classify objects such as a car, a person, or a bag. It is the least expensive image annotation service but lacks precision and consistency, especially when dealing with irregularly shaped objects and low resolution images.
Bounding Box annotation is commonly used in a number of cases:
Similar to bounding boxes, 3D cuboid annotation uses three dimensional shapes to mark the volume and depth of objects in 2D images. This technique places anchor points at each of its edges, feeding information to machines on what the object might look like.
Some of the use cases are:
Polygonal annotation encapsulates objects in irregular forms by marking them with numerous complex polygons. Unlike bounding boxes and 3D cuboid annotation, polygons better depict an object's real form. Polygons are highly flexible and adapt to a wide variety of shapes.
Key point annotation, also known as pose estimation and landmark recognition, allow models to capture more detail in detecting small objects and shape variations. In this service, data annotators mark images with key points, connecting them to portray an object’s shape and movement. Fitness and sports athletes make use of key point annotation to improve performance and prevent injuries. Capturing facial expressions for animation and security are also popular applications of key point annotation.
Other use cases include:
Line and spline annotation, also known as line or boundary detection, trains machines to recognize boundaries like road markings and edges. Data annotators usually resort to this tool when objects are too narrow to be annotated using boxes or other image annotation tools.
Line and spline annotation is also being used to program drones. Computer vision could teach drones to follow a particular course and avoid power lines1.
Other use cases:
While most image annotation services create an outline to identify objects, pixel-level labeling or semantic segmentation associate every pixel of an object to a corresponding class in a bigger image. Segmentation involves breaking down the multiple objects into segments according to their pixels. This image annotation tool is intensive as it gives a granular understanding of the objects in the image.
Considered as one of the most accurate image annotation tools2, semantic segmentation is applied in a number of use cases:
An image annotation process requires handling a large volume of data with high accuracy and speed and often faces many internal and external complexities.
Here are some of the common challenges faced during image annotation:
Due to these challenges, many companies prefer to outsource image annotation services to data annotation providers who can ensure the efficiency and accuracy of any image annotation project.
TaskUs provides AI training data services to manually annotate, collect, or evaluate various types of data by using customer tools and third party tools. We provide services and solutions that solve challenges in developing AI and accelerate deployment.
A globally leading Autonomous Vehicle (AV) company partnered with Us as they started to grow their operations. This entailed them to exponentially scale, refine, and enhance their AI training through high-precision data.
At TaskUs, we provide excellent image annotation services to train AI models. We have the best-in-class tooling to support a wide range of projects, content types and workflows. Our team employs multiple processes to meet and exceed training data quality standards and offers enterprise level security options for sensitive data or compliance needs.