Through large volumes of visual data collected, annotated, and evaluated by humans, different image annotation types allow computers to “see” the world.
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.
Types of Image Annotation Services
Bounding Box Annotation
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:
- Determining objects on the road to avoid collision for self-driving vehicles
- Auto-tagging products to optimize product searching experience for eCommerce
- Monitoring inventory for retail stores
- Vehicle damage detection for insurance claims
- Identifying stage of growth for plants in agriculture
- Image detection for robotics and drone imagery
3D Cuboid Annotation
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:
- Robotic training in determining accurate location and measurements for AI models
- Estimating objects alongside lanes when parking an autonomous vehicles
- Precise detection of indoor objects
- Determining construction and building structures
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.
- Mapping aerial views including identifying irregular bodies of water
- Outlining the anatomy of internal organs on CT scans
- Identifying items in a customer’s basket prior check out
- Defining important features like crop rows, tracking insect leg positions, and other details
- Specifying road edges, sidewalks, and more for autonomous vehicles
Key Point Annotation (Pose Estimation/Landmark Recognition)
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:
- Gesture and face recognition for security AIs
- Posture identification for AR/VR
- Sign language transcription
- Tracking instruments in robotic-assisted surgery
- Monitoring crane movement on construction sites
- Tracking the pedestrian movements on the street
- Detecting hand gestures of manufacturing workers
- Tracking the movement of livestock
- Detecting shopper actions in smart supermarkets
Line and Spline Annotation
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:
- Teaching warehouse robots to distinguish portions of a conveyor belt
- Recognizing lanes for self-driving cars
Semantic Segmentation (Pixel Level Segmentation)
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:
- Consumer-facing creativity tools
- Recognition of medical images for diagnosis, cell detection, and blood flow analysis
- Analysis of crop fields to detect weeds and specific crop types.
- Monitoring forests and jungles for deforestation and ecosystem damage to improve conservation efforts.
- Autonomous vehicles to determine driveable regions
What are the common challenges faced during an image annotation process?
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:
- Workforce: Any image annotation project needs an experienced team of annotators who can handle a vast amount of data and perform annotations with high efficiency. Depending on the project’s needs multiple quality checks might be required which can increase the team’s burden and further impact their productivity.
- Suitable annotation tools: For the success of any image annotation project, it is essential to have relevant annotation tools and software in place. However, annotation tools and technology often come at a hefty cost which many businesses ignore to save on their overall budget.
- Quality data: A machine learning model can only be as good as the AI data it’s trained with. However, getting high-quality, consistent training data sets for an image annotation project can be a challenging and expensive task.
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.
Case Study: Image Annotation for a Global Leading Autonomous Vehicle Company
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.
Outsource your Image Annotation Services with Us
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.