Machine learning is an application of artificial intelligence that has had a profound impact on our everyday life by significantly improving speech recognition, traffic prediction, and online fraud detection, to name a few, on a massive scale. At its core, computer vision, an application of machine learning, enables machines to “see” and interpret the world around them, much like humans do.
The performance of your computer vision model highly depends on the quality and accuracy of its training data, which is essentially composed of annotations of images, video, etc.
Image annotation can be understood as the process of labeling images to outline the target characteristics of your data on a human level. The result is then used to train a model and, depending on the quality of your data, achieve the desired level of accuracy in computer vision tasks.
This blog post covers all you need to know about image annotation to make informed decisions for your business. Here are the questions this blog post will be covering:
- What is image annotation?
- What do you need to annotate images?
- What are the different types of image annotation?
- What are some image annotation techniques?
- How are companies doing image annotation?
- Common image annotation use cases
- Wrap up
What is image annotation?
Image annotation is the practice of labeling images to train AI and machine learning models. It often involves human annotators using an image annotation tool to label images or tag relevant information, for example, by assigning relevant classes to different entities in an image. The resulting data also referred to as structured data, is then fed to a machine learning algorithm, which is often understood as training a model.
For example, you can ask your annotators to annotate vehicles in a given set of images. The resulting data can help you train a model that can recognize and detect vehicles and discriminate them from pedestrians, traffic lights, or potential obstacles on the road to navigate safely.
Autonomous driving is one example of how image annotation fuels computer vision. The use cases are countless, and we'll get back to them shortly, but first things first: What is it that you need to know before starting your annotation project?
What do you need to annotate images?
Different image annotation projects may have slightly different requirements. However, diverse images, trained annotators, and a suitable annotation platform are the building blocks of every successful annotation project.
You need hundreds, if not upwards of thousands of images to train a machine learning algorithm that makes fairly accurate predictions. The more independent images you have, the more diverse and representative of the surrounding conditions they are, the better for you.
Suppose you want to train a security camera to detect crime activity or suspicious behavior. In this case, you will need images of the given street from different angles, in different lighting conditions to create a reliable model.
Make sure your images cover almost all possible conditions to guarantee precision in prediction results.
A team of trained and professionally managed annotators is necessary to drive an image annotation project to success. Establishing an effective QA (quality assurance) process and keeping communication open between the annotation service and key stakeholders is crucial for effective project execution. Providing the workforce with a clear annotation guideline is one of the best data labeling practices, too, since it helps them avoid mistakes before they are set for training.
Also, make sure you provide regular feedback to your workforce for a more effective QA process and create an environment where everyone feels encouraged to speak up and openly ask for help when needed. Try to provide as detailed feedback as possible and always keep in mind its influence on possible edge cases.
Suitable annotation platform
Behind every successful image annotation project is a functional and user-friendly annotation tool. When looking for an image annotation platform, make sure it has the tools needed to cover your ongoing use cases.
Need a grouping experience in the editor your annotators are using? Voice your concerns. Maybe that's something the creators behind the tool can provide you with the following product release. An integrated management system and quality management process are also necessary to track project progress and manage project quality.
Keep in mind that you may encounter technical issues, so make sure the image annotation platform you choose provides technical support through documentation and a dedicated 24/7 support team. In fact, that's a major reason why industry-leading companies trust SuperAnnotate with image annotation.
Quality for users
An efficient image annotation platform must be designed to minimize miscalculations or misplaced labels in the data. Ideally, it should upkeep remote user management while also streamlining and elevating the experience of those who are able to assess the annotators’ jobs.
An innovative and advanced annotation platform should lessen and detect human error, as well as foster the delivery of more annotated items in less time by automating complex annotation processes.
What are the different types of image annotation?
Moving forward, let's go over the categories of image annotation we often encounter. While the following types of annotation are different in essence, they are definitely not exclusive, and you may dramatically increase your model accuracy by combining them.
Image classification is a task that aims to get an understanding of an image as a whole by assigning it a label. All in all, it's the process of identifying and categorizing the class the image falls under as opposed to a selected object. As a rule of thumb, image classification applies to images where there is one object present.
Unlike image classification, where a label is assigned to an entire image, object detection is the practice of assigning labels to different objects in an image. As the name suggests, object detection identifies objects of interest within an image, assigns them a label, and determines their location.
When it comes to object detection tasks for computer vision, you can either train your own object detector with your own image annotations or use a pre-trained detector. Some of the more widely used approaches for object detection cover CNN, R-CNN, and YOLO.
Segmentation takes image classification and object detection a step further. This method consists of sectioning an image into multiple segments and assigning a label to each segment. In other words, pixel-level classification and labeling.
Segmentation is used to trace objects and margins in images and is commonly utilized for rather complex tasks that call for a more developed precision when sorting inputs. In fact, segmentation can be viewed as one of the most pivotal tasks in computer vision, which can be broken down into three sub-groups:
Semantic segmentation consists of dividing an image into clusters and assigning a label to every cluster. It is the task of collecting different fragments of an image and is considered a method of pixel-level prediction. There is basically no pixel that doesn't belong to a class in semantic segmentation.
To sum it up briefly, semantic segmentation can be understood as the process of classifying a specific aspect of an image and excluding it from the remaining image classes.
Instance segmentation is a computer vision task for sensing and confining a specific object from an image. It is a distinct practice of image segmentation as it mainly deals with identifying instances of objects and establishing their limits.
It is also very much relevant and heavily used in today’s ML world as it can cover use cases such as autonomous vehicles, agriculture, medicine, surveillance, etc. Instance segmentation identifies the existence, location, shape, and count of objects. You can use instance segmentation to point out how many people there are in an image, let's say.
Semantic vs. instance segmentation
Since semantic and instance segmentation are often confused, let's define the difference between them through an example.
Imagine we have an image of three dogs requiring image annotation. In the case of semantic segmentation, all of the dogs will belong to the same "dog" class, whereas instance segmentation will also provide them with unique instances, as three separate entities (despite being assigned the same label).
Instance segmentation is especially useful in cases where you're tasked with separately monitoring objects of similar type, which points pretty much explains the instance is one of the most challenging ones to comprehend out of the remaining segmentation techniques.
Panoptic segmentation is where instance segmentation and semantic segmentation meet. It classifies all the pixels in the image (semantic segmentation) and identifies which instances these pixels belong to (instance segmentation). In the panoptic segmentation task, you must categorize every pixel in the image as going to a class label, yet you also need to categorize which instance of that class they go with.
In our example, all the pixels in the image will be assigned labels, but each dog will be counted separately. In contrast to instance segmentation, every single pixel in panoptic segmentation has an exclusive label corresponding to the instance, which in turn means that no instances overlap.
What are some image annotation techniques?
There are a number of image annotation techniques, though not all of them will be applicable to your use case. Getting a firm grasp of the most common image annotation techniques is crucial to understanding what your project needs are and what kind of annotation tool to use to address those.
Bounding boxes are used to draw rectangles around objects such as furniture, trucks, and parcels, and it is, in general, more effective when such objects are symmetrical.
Image annotation with bounding boxes helps algorithms detect and locate objects, which is what the autonomous vehicle industry relies on, for example. Annotating pedestrians, traffic signs, and vehicles help self-driving cars navigate safely on the roads. Cuboids are an alternative to bounding boxes, with the only difference being that they are three-dimensional.
When it comes to functionality, bounding boxes make it significantly less complex for algorithms to catch what they're looking for in an image and subordinate the identified object with what they were initially skilled for.
Polylines are probably one of the easiest image annotation techniques to comprehend (along with the bounding box), as it is used to annotate line segments such as wires, lanes, and sidewalks. By using small lines joined at vertices, polylines are best at locating shapes of structures such as pipelines, rail tracks, and streets.
As you might have guessed, on top of the applications mentioned above, the polyline is fundamental for training AI-enabled vehicle perception models allowing cars to trace themselves in the large road schemes.
Polygons are used to annotate the edges of objects that have an often asymmetrical shape, such as rooftops, vegetation, and landmarks. The usage of polygons involves a very specific way of annotating objects, as you need to pick a series of x and y coordinates along the edges.
Polygons are often used in object detection and recognition models due to their flexibility, pixel-perfect labeling ability, and the possibility to capture more angles and lines when compared with other annotation techniques. Another important feature of polygon image annotation is the freedom that annotators have when adjusting the borders of a polygon to denote an object’s accurate shape whenever it is required. In this sense, polygons are the tool that best resembles image segmentation.
Key points are used to annotate very specific features on top of the target object, such as facial features, body parts, and poses. When using key points on a human face, you would be able to pinpoint the location of the eyes, nose, and mouth.
More specifically, it is commonly used for security purposes as it allows computer vision models to read and distinguish human faces quickly. This feature allows key-point annotation to be widely used in facial recognition use cases, emotion detection, biometric boarding, and so on.
How are companies doing image annotation?
Image annotation is a significant investment in your AI efforts that costs resources like time and money, so carefully consider your project size, budget, and delivery time before choosing how to carry out your image annotation project.
Here are three ways how image annotation could come off in your pipeline.
One way is managing your image annotation project with the resources available at hand. You can either have in-house annotators do the job or annotate yourself if it's a small-scale experimental project.
If you have a team of annotators, make sure there's also a QA process involved as, in this case, you share responsibility for errors in data. To avoid having an increasing number of errors and subsequently poor model performance, your annotators will need proper training, instruction, and expert guidance. So, if you're leaning towards a faster way to annotate images while maintaining a high labeling quality, consider outsourcing your project.
Leave it to the experts for the delivery of quality results on time. When outsourcing to image annotation service providers, you gotta be extra picky in the workforce to ensure they are well-trained, vetted, and professionally managed to save yourself more than a headache. Better yet, run a pilot project to evaluate the performance and see if the results are in line with your project objectives.
If your data is too niche-specific, say you have DICOM images that need medical expert annotators, the teams may lack subject-matter expertise. SuperAnnotate has got all of that covered, on top of putting the security of your datasets above everything. Too good to be true for a single platform? Let's go ahead and book your free pilot!
If you’re lacking resources, you can always crowdsource your image annotation project. Using crowdsourced solutions for computer vision or data labeling services is a commonly used method that is time-saving and affordable at scale. Sometimes the downside of this solution is insufficient or poorly organized quality control. In any case, make sure you keep communication open and provide consistent feedback if you decide to move on with this solution.
Common image annotation use cases
By now, we've explored how image annotation is being used to build technologies that you’re using in your everyday life; the applications can range from the simplest of activities, such as your iPhone being unlocked because it recognizes your face, to robots performing various tasks across different industries.
Let’s explore some of the most common use cases in the coming sections:
As we already touched upon, image annotation is used in developing facial recognition technology. It involves annotating images of human faces using key points to recognize facial features and distinguish between different faces.
As it is being further developed, face recognition technology is becoming more and more common in various areas, whether it be access control for our mobile devices, smart retail and personalized customer experiences, security and surveillance, or other sectors.
Security and surveillance
Another common image annotation application is surveillance to detect items such as suspicious bags and questionable behavior. Image annotation for security became extremely beneficial for the greater public as it took procedures such as crowd detection, night vision, and face identification for burglary uncovering to another level in the best way possible.
Agriculture technology relies on image annotation for various tasks, such as detecting plant diseases. This is done by annotating images of both healthy and diseased crops. Measuring a crop’s growth rate is one of the most important aspects of attaining prime harvests, and image annotation can now offer farmers timely observations of growth rates across massive areas.
Not only does this method save the farmers more time, but it can also save them more money, as it can help detect common issues in soil and vegetation in early stages; other issues may include nutrient deficiency, water shortage, bug issues, and poisonousness. AI-enable agriculture technology can also assess the ripeness of fruits and vegetables, which in return, can lead to more profitable harvests.
Image annotation has immense use in the medical field. For example, by annotating images of benign and malignant tumors using pixel-accurate annotation techniques, doctors can make faster and more accurate diagnoses.
Medical image annotation, in general, is being used to diagnose diseases such as cancer, brain tumors, or other nerve-related disorders. Here, annotators highlight the regions that need extra care, and this is done through the usage of bounding boxes, polygons, or whatever technique is applicable to that particular use case.
With the availability of data today, healthcare professionals are able to provide more accurate information to their patients, as predictive algorithms and image annotation techniques are now offering better predictive models.
Although humans are creating advanced technologies for robotics, automating a lot of human-involved processes, we still are in need of further assistance and cannot do everything on our own. Image annotation is helping robots to distinguish between various types of items which is realistic thanks to human input — annotators, in particular.
Line annotation is also of great importance in robotics, as it is being used to help differentiate between diverse fragments of a production line.
Robots depend on image annotation to perform tasks such as sorting parcels, planting seeds, and mowing the lawn, to name a few.
With the rising demand for autonomous vehicles, it goes without saying that the industry is rapidly expanding. How come? Through the appliance and assistance of data annotation techniques and labeling services. As the use of labeled data helps to make different objects more predictable by AI, annotation precision becomes the driving force for data-centric model creation. These high-quality annotated datasets are fed into the model/algorithms, iterated upon, and then — if spotted impurities — are reannotated, checked for quality (QA) after deployment and trained again to ensure the desired level of accuracy for autonomous vehicles.
Object detection and object classification algorithms are the ones responsible for autonomous vehicles’ ability to perform computer vision tasks and foster harmless decision-making. Because of these algorithms and labeled data, autonomous vehicles can easily recognize crossroads, provide emergency warnings, identify pedestrians and animals crossing the street, and even take control of the vehicle to help avoid accidents.
Despite the variety of image annotation techniques, only a few are actually applied to make training datasets in this sector. Bounding boxes, cuboids, lane annotation, and semantic segmentation are the main image annotation techniques that are used during the creation process. The latter assists the vehicle’s computer vision-based algorithm, which, in the end, makes scenarios easier to understand and contextualize for AI while also helping avoid possible collisions.
Nowadays, a large number of industries are moving ahead because of aerial/drone imagery. A drone’s main function is to collect data through sensors and cameras and use that same data to analyze information, and when extended to AI applications, it requires image and video annotations for training data. Aerial image annotation comprises labeling the images which were taken by the satellites/drones and then using them to train computer vision models to study the important characteristics of any particular domain.
The AI-enabled drone industry is involved in solving serious recurring issues in various spheres such as agriculture, construction, nature conversion, security and surveillance, fire detection, and much more. Each of these industries deserves an article of its own to cover all functionalities of drone imagery, so let’s narrow it down to one.
When zooming in on drone imagery functions for nature monitoring and conservation, for instance, the benefits seem endless. Researchers, conservationists, environmental engineers, and many others rely on drones to efficiently capture their preferred environmental data, which they later use to serve their project needs. One of the reasons why drones are preferred in this specific field is because of their efficiency in quickly gathering data which would otherwise require a human to fly out to destinations and take footage/pictures manually, making it both expensive and time-consuming.
Drones are used in a handful of ways to protect wild species and their habitats from going extinct, which usually encompasses annotated data of target areas and consecutive training. A more particular example is wildfire management to locate and detect fires to prevent them from causing further damage. AI-powered drones can detect fires way quicker than humans, come up with smarter and safer solutions, and prevent hazards before they become fatal.
Similar to the sectors mentioned above, the insurance industry was also highly influenced by AI and data annotation. When it comes to getting things done, both insurance workers and customers want fast results, and this is when AI enters the picture. AI’s ability to collect and analyze data takes a huge load off and makes inspection and evidence-gathering processes faster.
Another advantage is AI’s ability to fight against potential fraud through behavior analytics and pattern analysis. It is safe to say that AI risk management systems revolutionized insurance business models in terms of risk personalization as they effectively handle all the risk management of current and new insurance settlements. Such fraud detection applications can also detect any shortcomings with an application, which in turn makes it easier to spot irregular customer activities and behavior.
Artificial intelligence and machine learning are the driving forces of the modern tech environment, impacting all industries, from healthcare to agriculture, security, sports, and much more. Image annotation is one of the ways to create better and more reliable machine learning models, hence, more advanced technologies. So, the role of image annotation cannot be overstated.
Remember that your machine learning model is as good as your training data. So if you have a large amount of accurately labeled images, videos, or just any data, you can build a model that delivers excellent results and serves humans for the better.
Now that you know what image annotation is, the different image annotation types, techniques, and use cases, you can take your annotation project or model creation to the next level. Are you ready to get started?