As machine vision and computer vision continue to evolve, they’re becoming more and more integrated into our everyday lives.
Machine vision can help improve product quality, smart cameras can alert managers when immediate action is necessary, and robots with machine vision can automate simple tasks, allowing teams to focus on higher-level responsibilities. Users can begin to perceive AI systems as intelligent links in their workflows. However, those systems’ intelligence is, in fact, artificial and depends on accurately annotated images to provide them with the ability to understand, learn, and perform.
Image annotation is the process of associating the whole image or parts of an image with a predefined set of labels. Image annotation is frequently used for image classification, image detection, and image segmentation for machine learning and computer vision models.
What Are the Main Image Use Cases Enabled by Image Annotation?
When an AI project team is evaluating image annotation services, it’s essential to understand the differences between different types of image annotation tasks. The right kind of annotation is vital to the success of your project. In general, there are three types of image annotation use cases:
Image classification defines the class of an object within an image. Single label classification is the process of associating an entire image with one label, such as “dog” or “cat.” An image may have multiple classes of objects- for instance, a dog and a cat. Multi-label image classification is the task of assigning a set of labels to the objects or attributes in an image.
Data labelers create datasets to train an AI model simply to identify or locate objects within an image or video based on location, shape, or other variables. For example, you can label images to train a model to detect a traffic sign as an autonomous vehicle moves on a street.
This type of data annotation enables a deeper understanding of images. Semantic segmentation involves creating “masks” that cover the exact shape of each object within an image, providing granular details such as shape and comparative size. This annotation type covers every image pixel and uses color codes to differentiate between the objects.
Types of Image Annotation Shapes
In addition to different approaches to image annotation, the shapes that data annotators use also give machine learning and machine vision systems specialized capabilities. Different shapes used in image annotation include:
Lines and Splines
Annotators draw lines or curves showing lane boundaries on the road to create datasets that can train AI models for autonomous vehicles to stay within their lanes.
Using this image annotation method, labelers draw 2D boxes around objects. The boxes have one or more labels that allow the AI model to identify the object and its attributes. Alternatively, image annotators can use 3D bounding boxes, aka “cuboids,” to enclose the object and anchor points to show the object’s length, width, and depth.
This type of image annotation is typically used for objects with irregular shapes. Similar to bounding boxes, the polygon is labeled to identify the object and its attributes.
Data annotators draw points to mark facial traits or highlight gestures or postures. This type of annotation gives computer vision systems facial recognition capabilities, allows them to detect emotion, and enables form assessment in sports and fitness applications.
Image Annotation for Video Tracking
When an AI model must be trained to use video data, 2D or 3D bounding boxes drawn around objects of interest or semantic segmentation in every video frame can teach the model to recognize vehicles, ships, pedestrians, and other objects moving throughout the video.
It’s worth noting that different types of data annotation require different skills and expertise. AI depends on humans in the loop that are well-trained and experienced. A data annotator must understand the type of labels and shapes required to clearly train an AI model to address the problem it is designed to solve.
The best image annotation services also include quality assessment processes that review annotated data, spot and report errors (whether occasional, systematic, or misinterpretation errors), and correct root causes.
The image annotation team’s quality assessment procedures should also use appropriate quality metrics, such as intersection over union (IoU) mean intersection over union (mIoU), precision (P), recall (R), and average precision (AP). Tracking metrics allows image annotation service providers to ensure they are working accurately and meeting the quality standards for the project.
How Machine Learning Relies on Image Annotation
Simply put, the relationship of machine learning models with data annotation is one of dependency. Data annotation makes images usable to machine learning models, which can’t see them as humans can. Without annotated data, the ML model won’t know how to recognize, identify, and classify objects. The annotations or labels train an AI model to make sense of the data in the images until it can do so on its own.
Furthermore, the adage “garbage in, garbage out” is a reminder that if AI training datasets aren’t properly annotated, the model won’t produce desired results. Training an AI model with data that’s labeled carelessly or inaccurately will stand in the way of the model working correctly. Conversely, exceptionally precise annotation leads to better AI performance.
Which Industries and Use Cases Rely on Image Annotation?
AI machine vision systems are becoming more ubiquitous, and the demand for image annotation services is increasing across a wide range of industry segments. As a result, image annotation service providers provide specialized services for industries and use cases such as:
This highly specialized type of data annotation trains AI models for use in the healthcare sector in areas including radiology, dermatology, ophthalmology, and pathology.
Data annotators specializing in thermal images will interpret images and label areas based on temperature, preparing models for emergency response, agricultural, industrial, and other use cases.
Drone and satellite imagery can train AI models to predict harvest yields, assess soil quality, and pinpoint specific areas where pesticides or herbicides should be applied.
AI models can enable product search by image or retail robots that can identify products and quantities on the shelves and notify managers when it’s time to reorder.
Optical Character Recognition
Data annotation can help OCR systems learn to “read” and adapt to specific use cases, such as recognizing traffic signs, license plates, documents, or ID cards.
A high-profile use case for image annotation is training AI models for autonomous vehicles. Whether used within warehouses, at ports for international shipments, or on the open road, autonomous vehicles must be properly trained to recognize their lanes, traffic signs and signals, other vehicles, pedestrians, and objects they may encounter to ensure safe operation.
How Do Companies Scale Image Annotation?
A common challenge that ML project teams face with image annotation is how to scale. A project can experience delays when a team realizes they need 100,00 images instead of 10,000 to train an AI model or their organization decides to move forward with additional AI projects.
Businesses can choose from three possible routes to scale image annotation:
Keep image annotation in-house
This option gives AI project teams the most control over data privacy and security. However, it requires dedicating employees to the project. Given that image recognition, classification, and segmentation can require hundreds of thousands of images to train an AI model adequately, companies may find that keeping image annotation in-house isn’t financially feasible or even possible with the staff hours available.
Businesses can recruit image annotators from global platforms like Amazon Mechanical Turk or Upwork. It’s a low-cost option, but the work is inconsistent and inaccurate in many cases. A business is left with few options other than to redo annotation or scrap the project.
Outsourcing to an experienced image annotation service gives an AI project the benefit of trained and skilled data annotators, the ability to scale quickly, and quality control.
Teams can also benefit from processes that they can’t execute in-house or that aren’t available when crowdsourcing, such as:
- Predictive technology. This enables more efficient annotation by using a system that predicts annotations that are then refined by humans in the loop, accelerating annotation and enabling annotations at scale.
- Image selection. This technology allows for discarding images that are too similar to others that have been labeled, ensuring that the dataset has the right degree of variability and that the AI model will perform better.
There isn’t one right answer for every AI project. However, it’s vital to consider the pros and cons of each,
Learn More About Our Image Annotation Services
As more businesses and organizations deploy machine learning and machine vision systems, the demand for precise, accurate image annotation will increase. It’s the key to solutions that perform as intended – in some cases, with people’s health and safety in the balance.
Sigma helps data science teams strategize and scale high-quality training, validation, and test data with our global team of experienced annotators and various image annotation technologies. We offer an end-to-end approach to image labeling services, helping you determine the best way to label data to train your AI model for your use case. Contact us to learn more.