Computers today use a variety of sensors to gain continuous data for analysis. One category of those sensors includes visual data generation devices like cameras. The data they produce is analyzed by Artificial Intelligence systems utilizing computer vision technology. The system must be trained to do this using labeling databases through image and video annotation.
Image annotation is the method by which the target subject (that the system needs to identify in a given image) is marked in any data sample. A similar approach is applied to videos when identifying the target subject in individual frames, called video annotation. Machine learning algorithms pick up on these demarcations over time, eventually learning to do it themselves in any random real-world data sample. However, owing to the rising requirement for these techniques and their inherent complexity, enterprises often look at third-party image and video annotation services to get the job done.
Read on to learn about some applications where these data annotation techniques are already being used.
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Self-Driving vehicles
Autonomous driving vehicles, better known as self-driving vehicles, are heralded as the future of transportation. They already exist in a semi-autonomous form in many everyday vehicles. Such vehicles rely on various sensors, like cameras, RADAR, LIDAR, and SONAR, to gather visual information about their environment.
The real-world data gathered is complex, especially since motion is involved and surrounding objects deviate from their expected standard characteristics. The self-driving AI should be able to analyze and understand the gathered data and make quick decisions to steer the vehicle, all at near instantaneous speeds.
The algorithms need to develop this decision-making ability through thorough, accurate, and exhaustive image annotation. Multiple data samples of relevant roadside images and video should be input for training, with all objects like road signs, trees, and people, clearly demarcated. Only then will the vehicle reach the intended Level 5 (fully autonomous) driving capability.
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Healthcare
The medical field is rapidly getting automated via robotics and AI. Radiology, for example, uses AI to detect abnormalities in images of scans like X-rays and MRIs.
Since radiologists are often swamped with many such images due to the high workload, it may cause them to misdiagnose, leading to the wrong treatment being administered to the concerned patient. An AI can assist in such circumstances by providing an accurate early diagnosis by examining the scan results.
The other area where visual data is important in healthcare is remote surgery. Surgeons can view and operate on a patient from a remote location using AI-assisted tools. The AI should be able to distinguish between the various parts of the patient to render the right care. Another use is in pharmaceuticals, where the AI can scan the many sheets and cases of medications passing on the assembly line to remove faulty ones.
Remote medicine delivery also relies on drones to navigate accurately to their respective destinations. Researchers use AI to detect various phenomena in visual data provided by instruments like microscopes. Each scenario has AI trained using image annotation to perform its respective functions.
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Security
CCTV cameras and other such monitoring devices play a significant role in keeping us safe today. Their feed is mostly provided to a monitoring station where security guards view them and take action, if required. However, that becomes difficult when there are a large number of feeds to monitor and a high density of people.
AI has been roped in to take over the monitoring instead, identifying anomalies in behavior patterns and pinpointing wanted persons’ faces in a crowd. The technology is also extended to home security since it’s likely that there isn’t a security guard at the doors of individual homes. Image and video annotation are vital to developing such an AI, by training it to discern the various features of faces and other pertinent points of interest.
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Logistics tracking
Most businesses are now global, requiring companies to ship their raw materials and finished products across vast distances via air, land, and sea. The cargo needs to be monitored to ensure that it reaches its destination in time and in good condition. Industrial Internet-of-Things (IIoT) devices are used for this purpose, whose data is fed to AI systems for analysis. Image and video annotation can train these systems to recognize the various vehicles and cargo containers and produce reports regarding their location.
Conclusion
The impact of AI will continue to be felt across sectors and functions. Hence, so will the importance of video and image annotation services, along with their video counterpart. Your business will stand to gain multiple advantages if you opt to go with annotation and get the AI you need for its many functions presently. It’ll make your profits future-proof.
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