Real-life applications of AI technology have become mainstream, evident in self-driving cars, automated medical systems, or robots and drones helping farmers on the field. In fact, the market for data solutions in AI and machine learning is predicted to reach $1.2 billion by 2023. Image Annotation is a critical cog in the AI wheel. It is the process of attaching labels to an image. Though this methodology isn’t novel, it has garnered significant traction as a critical unit of computer vision technology over the last five years. Its uses can be found in numerous industries, including e-commerce, agriculture, healthcare, security, and surveillance. Object detection, face recognition, website chatbots- we can find numerous examples where image annotation plays a critical role.
Let’s dive into the significance, use cases, and applications of image annotation across popular industries in 2022.
What is the Significance of Image Annotation?
Annotating an image means identifying elements inside the region of interest in a digital image. Multiple labeled images are fed to a computer vision model so that it can learn to recognize things (like pedestrians, cars, buses, water hydrants, and animals in an image of traffic). This is done to teach a computer to recognize things belonging to the same classes in visual scenes.
|Bounding boxes, 3D cuboids, lines and splines, and polygonal segmentation are common image annotation types.|
Annotating images is a step in the machine learning process that aids in building a database with which you can train a computer vision model to autonomously recognize images.
The images you use to develop, verify, and evaluate your computer vision algorithms will have a big impact on how well your AI project turns out. The entire process of image tagging should be done carefully and precisely to teach an AI system to detect items similarly to how a human does. Your machine learning models will likely perform better if your image annotations are of higher quality.
Popular Business Applications of Image Annotation
Businesses across numerous industries are seeking AI/ML-based solutions for various purposes. For instance, face recognition is a feature in almost every smartphone these days. Automated speed violation tickets are another common example where image-based automation is utilized. Subsequent data collection has also increased, leading to a rising need for image annotation.
Let’s look at image annotation’s applications in various business sectors:
Let’s face it! More than 85% of online users are less inclined to visit the same website again if they have a negative experience with it.
Every customer wants to feel special. Customers of today don’t prefer cookie-cutter approaches. They expect personalized digital experiences and convenience at every step. AI/ML facilitate this through appropriate annotation.
The use of data or picture annotation in eCommerce is demonstrated by a few applications, including visual search, smart predictive marketing, and hyper customization.
For instance, e-commerce platforms that use Computer Vision incorporate a visual search feature. Customers have the option of taking or uploading a photo of the item they are interested in. AI assesses the item’s characteristics with the use of image annotation and recommends relevant products in both online and offline stores. It would ultimately help customers to tailor their search results, boosting sales revenue and providing chances for more product suggestions.
The process of annotation makes sure that products are accurately labeled and classified for improved search relevancy for product suggestions. It can also help offline stores manage their inventories and improve cashier-less checkout procedures.
|For instance, if a consumer saves an image of a product but forgets the specifics, he can easily locate the goods by dragging the saved image into the search bar.|
The technology of image tagging is employed in the healthcare sector to train AI algorithms for medical image analysis and diagnostics, assisting doctors in saving time, improving patient outcomes, and making more informed judgments. Deep learning models are strengthened by image annotation, which increases the precision of diagnosis and raises the standard of healthcare.
Image annotation software such as the popular CVAT (Computer Vision Annotation Tool) in healthcare solutions uses CT scans, MRIs, and other diagnostic technologies to focus on patterns and problems like tumors or hairline fractures. This technique gives the learning algorithms real knowledge (data), enabling the model to recognize these diseases when similar medical images are presented to the system.
Such types of image annotation applications may accurately predict diseases at the microscopic level, including cancer and typical bone fractures. This can dramatically shorten patient wait times and minimize backlogs.
3. Transportation Sector
Various automotive companies like Tesla are highly devoted to boosting their technologies for autonomous taxis and self-driving cars by leveraging computer vision. Moreover, the aftermath of the COVID-19 pandemic has raised the demand for audio/visual technologies, including self-driving cars and drones that transport groceries.
However, the machine learning algorithms that power these vehicles must be incredibly reliable for autonomous vehicles to be deployed with confidence. In such a case, the technology of image annotation enables automobile manufacturers to design intelligently-engineered applications for autonomous vehicles. Since digitization is accelerating across the globe, the process of image tagging and data labeling initiatives are the blooming future of the transport industry.
Farmers hardly ever discuss yields when bad weather occurs, crops fail, or when a pandemic strikes the world. Managing numerous farming procedures becomes more difficult all of a sudden because the majority are not digital.
Thanks to the deployment of image annotation in the agricultural sector, a tremendous transformation has taken place in the world of farming. Everything is marked with annotations for machines to recognize and act appropriately, including plants, crops, fruits, and even the soil. The technology can also be used to label insects, undesirable plants, wildflowers, etc. Later, the algorithm will detect such elements automatically and issue alerts to clear the ground so that the crop may flourish.
|The employment of robotics, drones, GPS sensors, and autonomous vehicles to speed up farming procedures that were previously entirely manual is one example of precision agriculture.|
Annotating images is also essential for determining the state of the field and the health of the soil. It is beneficial for geo sensing to assess the state of the soil and help companies decide when is the best time to farm or harvest.
The platform will assist farmers in the future as they become agricultural technologists, utilizing data to maximize yields right down to individual plant rows. Farmers will be able to switch to precise cultivation for improved crop quality and production while consuming fewer resources.
The security and surveillance sector can benefit greatly from image annotation. One can train the computer vision models to examine human behavior and recognize faces in crowds to stop significant crimes. You may also teach them to count individuals, recognize demographics, and perform other useful tasks.
For example, CCTV cameras are used for security or surveillance, and the images they collect are annotated for use in teaching smart cameras through AI and ML. These visual annotations make it easier to distinguish between a normal video and when anything unusual occurs, such as a break-in, a theft, a fire, or an unlawful trespass.
Moreover, smart cameras are programmed to recognize these movements whenever something occurs and to sound alerts to notify security. Thermal cameras with night vision are also available, and these can record motion when there is no light.
One can use image annotation in jails, malls, government and private sectors, radar centers, military base camps, and key infrastructure. However, it is also useful for protecting commercial buildings such as banks, ATMs, airports, and train stations.
The industrial transformation brought about by artificial intelligence and machine learning is driven by Image annotation. We can witness its impact on every sector- from security to agriculture and healthcare. From preventing crimes to advancing agricultural programs and strengthening healthcare services, image annotation is proven to be a necessity.
Therefore, it would be accurate to conclude that image annotation has a massive role to play in the future of AI & ML.