Image Annotation is gaining traction across several industries. It labels images either manually or automatically to train supervised Machine Learning (ML) models for computer vision tasks. This article covers the various annotation techniques and how they are used across different industries.
Image annotation is both crucial step and challenging in computer vision. It is the foundation of many AI products we use, a type of data labeling essential in a supervised ML task.
Similar to how parents teach their children about the different objects in their surroundings and how to identify it, ML models learn and produce results based on how accurate the labels we feed them during training are.
For AI developers and researchers to achieve the ambitious goals of their projects, they need access to enormous amounts of high-quality data. For computer vision purposes, the quality of images you use to train, validate, and test algorithms have a significant impact on the AI project’s success. Each image in your dataset must be carefully and precisely labeled to train an AI system to recognize objects in the same way that humans can. Better annotations lead to accurate ML performance.
While the volume and variety of your image data are likely to increase on a daily basis, getting images annotated to your specifications can be a challenge that slows your project and, as a result, your time to market. Carefully consider your image annotation techniques, tools, and workforce.
What is image annotation?
Image Annotation (a.k.a., Image Tagging) is a subset of data labeling where images are tagged with metadata and other characteristics for training purposes.
A simple example of this is providing human annotators with images of fruits and having them label each image with the correct name. These annotated images (a.k.a., ground truth data) would then be fed into a computer vision algorithm. The model would then be able to distinguish fruits from unannotated images after being trained.
The COCO dataset by Microsoft Corporation is the most commonly used dataset.
It consists of a large number of daily scene images containing everyday objects. With pixel-level annotations, it provides richer data for object detection, semantic segmentation, and text annotation, facilitating training and evaluation of object detection and segmentation algorithms.
Types of Image Annotation
In bounding box annotation, human annotators are tasked with drawing a box around specific objects in an image after being given the image. Every edge of the object should be as close to the box as practical. The development of autonomous vehicles is one particular use for bounding boxes. Annotators would be instructed to outline objects in traffic images, such as vehicles, people, and bicycles.
The underlying theory for polygonal segmentation is merely an expansion of the theory underlying bounding boxes. The location and boundaries of an object can be determined with much greater accuracy thanks to complex polygons rather than a simple box to direct a computer vision system where to look for it.
Polygonal segmentation has an advantage over bounding boxes: it removes a lot of the background noise and extra pixels that could confuse the classifier.
Creating image annotations using polygons helps to observe the precise shape of an object by identifying coarse, irregularly shaped objects in images and videos as training data for AI models. It is suitable for accurately detecting objects like road sign boards, logos, various human poses in sports analytics, and other interesting objects.
3D cuboids are similar to bounding boxes with additional depth information about the object. With 3D cuboids, you can get a 3D representation of the object, allowing systems to distinguish features like volume and position in a 3D space. Typically, anchor points are put at the item’s boundaries and a line is used to fill in the space in between the anchors.
The use of 3D annotation is in a variety of industries because it enables the perception of depth and volume. It is significantly more difficult to annotate than 2D data, but it provides exceptionally better insights when 2D visual data is insufficient. A use-case of 3D cuboids is in self-driving cars where they use depth information to measure the distance of objects from the car.
Semantic segmentation divides an image into distinct areas while giving each pixel in the image a label. The key idea is each pixel that makes up a region receives a label from the image classifier, and each region is defined using semantic information.
An example of this is segmenting everything in the image by roads, buildings, cyclists, pedestrians, obstacles, trees, sidewalks, and vehicles when working with training data for autonomous vehicles.
Key-point and Landmark
Key-point and landmark annotation is used to detect small objects and shape variations by creating dots across the image. This type of annotation is useful for detecting facial features, facial expressions, emotions, human body parts, and poses.
Landmarks are utilized in gesture recognition, human pose recognition, item counting, and human pose datasets in addition to the facial dataset.
How Industries Benefit from Image Annotation
In 2021, the global AI training dataset market was valued at USD 1.4 billion and is expected to grow by 22.2% annually through 2030. By using high-quality, human-powered data image annotation, companies can build and improve their AI digital implementations.
Here are some of the key industries positively impacted by different image annotation techniques:
Undoubtedly, autopilot is one of the most recent significant achievements in Machine Learning.
To ensure the safety and efficiency of these vehicles, they must be powered by sophisticated ML algorithms. With image annotation, automobile manufacturers can design smart applications for these autonomous vehicles.
Companies like Tesla and Audi use different approaches for annotating images. This includes auto-labeling, an AI tool that can annotate images and videos automatically.
Annotating images can greatly enhance customer experience and make it easier for them to find the products they are seeking. It ensures that products have the correct information and are categorized correctly for better search relevance for product recommendations.
Image annotation can also help offline retailers by improving the app’s image search functions or inventory management to keep store shelves stocked with the products that customers want. Experienced image annotation teams annotate images of shelves, prices, brands, and products so businesses can track shelf management, identify misplaced items, and conduct price checks quickly. Smart checkouts are an important component of store robotization. They have the potential to reduce theft by monitoring checkouts in real time and providing information to security personnel. Alerts about missing products or empty spaces on the shelf in real-time is also a good use-case.
In healthcare, AI can help increase the precision of the diagnosis and raise the standard of care. Through CT scans and MRI, which both operate on the basis of well-trained ML models with significant medical image data, diseases including brain tumors, blood clotting, and a few neurological illnesses can be diagnosed. This can dramatically shorten patient wait times, minimize backlogs and eliminate the cost of expensive equipment.
Image annotation is completely changing the agriculture industry, one of the oldest industries on Earth. Computer vision systems can be trained to predict crop yields, determine plant health, optimize soil conditions, and much more. Image annotation would be central to these processes, allowing ML algorithms to pick up on specific cues, much like experienced farmers would.
Accurate picture annotation is needed for the exact detection of everything. Bounding boxes are a useful tool for robots or drones to capture images and videos in this situation. It can be used to assess the state of the soil and help companies and farmers decide the best way to grow and harvest.
The sport, gaming, and entertainment technology revolution uses cutting-edge AI tools to improve performance or provide fans with a more immersive experience, such as improved individual and team performance analysis, fx-video editing, or character motion control.
A good example is, labeling players from game footage using bounding boxes for training real-time tracking modules on fields. To make the human poses recognizable, landmark or point annotation is used while semantic segmentation image annotation can more precisely identify these things as belonging to a particular class.
Developing effective and efficient training datasets for Machine Learning can be time-consuming and resource-intensive for innovators. Outsourcing image annotation allows computer vision projects to gain access to precise training images while maintaining flexibility and oversight.
There are different image annotation tools and techniques that are commonly used today. The procedure must be compatible with both the computer vision tool’s use-case and the deep learning model’s architecture.
Tasq.ai specializes in annotation services for different industries. By integrating Machine Learning and human intelligence, we assist companies around the world in solving complex data problems, improving customer experience, and reducing costs.