Posted by The growth of Artificial Intelligence has been a boon for businesses across niches. The multiple advantages AI brings to the table have made businesses more efficient and upped their performance. With worldwide corporate investment in AI touching US$ 91 billion in 2021 and showing strong signs of a significant increase going ahead, it’s safe to say that this technology is necessary to sustain a business henceforth. If you’re looking to make it in any industry niche, you must include AI in your plans.
However, any AI model will be incompetent until trained well through a high-quality dataset. This is where image annotation comes into the picture. Image annotation is a technique used to label datasets with information. AI systems study that dataset to build an understanding and gain their ability to think and act rationally, as humans do. It enables AI to capture and interpret visual data.
And the best part? AI with computer vision is just getting started in all types of business-related applications. So the time is ripe for you to start performing Image Labeling/ annotation for your video and image data and reap the benefits. Read on to learn the basics of the process and its potential to transform your business and the ways it could grow.
What Is Image Annotation and How Is It Different from Image Labeling?
If you’re new to all things AI and Machine Learning (ML), then this question may be on your mind. The answer is annotation, in this case of images and video, is how professionals tag important elements in image and video data samples to train an AI/ML model to identify that element. They use many sophisticated techniques like bounding boxes to mark those objects.
Depending on the application for which the development is occurring, they will use different types of annotations available for images, from simple classification to complex segmentation. Since large quantities of sample images are needed for training purposes, experts create ML algorithms that can perform image tagging by themselves via Deep Learning.
Labeling is not much different from annotation and the two terms are used interchangeably. The minor difference is that with annotation, there is just the tagging of various elements in the images, whereas labeling adds context as well. The tagging done by a developed AI model is also considered labeling since the machine itself adds labels to the object and it doesn’t undergo the annotation process to do it.
The Benefits Offered by Image Annotation To Businesses
The following reasons make Image annotation a must-have for your business.
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Improves Healthcare Administration
Healthcare is no longer a matter of just medical professionals performing the necessary functions to treat/operate on patients. It is also not just healthcare facility administrators rushing through the paperwork and doing their best to keep things accurate and seamless. With image annotation, AI is taking over such mundane and even delicate tasks in this sector to great effect.
Radiologists now have AI assisting them in detecting various ailments present in scan results. Surgeons have AI-operated robotic arms helping them perform remote surgeries. Personal health monitoring devices are connected to AI-based systems that can accurately monitor a person’s health before, during, and after treatment and create their complete health profile.
AI is also helping medical professionals create accurate transcriptions that don’t cause problems while claiming insurance. Medical institution maintenance professionals can rely on AI to check for contract terms and problems in delivery systems like oxygen pipes.
Image tagging is behind these AI systems as they have to be trained with numerous sample images of unhealthy vs. healthy body parts, medical documents filled with standard data vs. haphazard inscription, proper vs. broken equipment, etc. With more training comes improved AI functioning, and by extension, better healthcare administration at all levels.
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Keeps Your Premises and Equipment Safe
Security for real-world and virtual domain applications has progressed to become digitally driven. Technology adoption for security started as assistive tools for security personnel. While this aspect is still prevalent, AI is being incorporated increasingly to offset some manual security functions, with a push to replace them entirely. This push has generated demand for image labeling to become a key factor in the transformation.
CCTV cameras are no longer visual sensors that relay their feed to monitors in security rooms. Instead, they form a part of the entire security ecosystem of the building, acting as the eyes of a central AI. With the help of facial recognition, body shape, size, gait, and other physical properties, the AI can detect an unfamiliar person and behaviors. It can then flag them or take other measures permitted by security authorities.
Biometric access is another area of interest. Multi-factor authentication is the norm today, and things like facial, iris, fingerprint, and heat signature are used along with passwords for enhanced security. Image tagging is used to pinpoint the variations in these identifying factors to ensure that they are accurately recognized.
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Creates Better Designs
From architecture and interior decoration to cars and household items, design is a must. It makes the result attractive and trendy while keeping it in line with technical criteria like aerodynamics. AI is increasingly being employed to create robust and unique designs quickly, or recreate present designs to target a variety of audiences.
While presently the systems used function more like Augmented Intelligence that assists in reducing the workload of designers, the development trajectory is leading toward a full-fledged, independent design AI. And stating that visual elements are at the core of design would be an understatement. Therefore, the AI needs to not only interpret the technicalities of a design training sample but also try to infuse context into it.
Image labeling is how AI can accomplish this feat. Annotators working in conjunction with design professionals can mark important aspects of various design samples while also introducing context. Image Segmentation is one annotation technique that finds use here as it allows for contextual labeling. Tagged audio and text data can also be added whenever necessary to make the AI more expressive and expansive in its operation.
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Helps Ease Tracking
If you’re into eCommerce or any business that involves extensive logistics and supply chain management, you will appreciate the easing of tracking items at every point in the chain. Fortunately, AI is up to the task already, with warehouses and factory floors already using it to accurately track both items and personnel.
Be it for static or moving objects in these environments, AI for image-based tracking requires image annotation to function. The exact location of the container having a particular item with a specific SKU, its category, etc. can be known and logged into the warehouse logistics management software automatically. Automated warehouses can use image-annotated AI to run robots that move packages around, assisting the people there.
Other than for tracking relatively small objects, AI using computer vision can track large objects too like vehicles and shipping containers. Real-time satellite images can be fed to AI systems to monitor aircraft, ships, and trucks carrying your company’s cargo, differentiating them from others.
Personnel management software uses accurate geo-location and local tracking to ensure that employees are within set geographic limits, like a construction site during work hours. Image tagging helps tell them apart from managers who have to move about different locations, making such software more accurate and robust.
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Aids Finance Management and Investment
No business can afford to suffer inaccuracies, delays, or both when it comes to its finances. The pressure is much higher for those into trading and investments in stock markets. AI is the right tool for the job here as it excels at maintaining accuracy and speed at all times. It can skim through large volumes of financial records and nail every digit to the last decimal.
It can check for compliance with regulatory bodies and contracted parties and tally it with available accounting data to see if all clauses are satisfied. In the age of algorithmic trading, AI can handle the various tasks associated with stock trading like company performance checks, present share value, news articles about them that could influence their share prices, etc.
Image labeling comes to the fore here as this type of AI needs to be taught how to read graphs, finance documents, performance reports, market tickers, etc. accurately in real time. Otherwise, it will result in significant losses to investors.
Conclusion
The adoption of AI is progressing swiftly in all industries, with billions being invested each year by private and public entities alike. Your company will sustain, if not thrive if it also participates in this transformation. And the way to gain a steady foot through this door is to have image annotation services working to develop AI in that world for your applications. Your business will experience an increase in efficiency and a reduction in losses, leading to better ROI and future-proofing while gaining a better brand reputation as a trendy one.