Uses of image recognition are numerous and cut across different fields. AI/ML has enabled the speedy development of image recognition in the past decade and is becoming a core part of many industries and applications. It is already making a significant contribution to fields as diverse as medicine, farming, security, and entertainment. It is already making the world a safer and better place to be.
1. Security and Surveillance
Image recognition has a particular place of prominence in security and surveillance-related applications. Professionals are applying this technology in facial recognition systems, which are applied in security sectors such as in airports, government offices, and during public events or gatherings, to name but a few, to name and authenticate persons. It is a useful tool in law enforcement because it assists in revealing details of the suspect and criminal activity.
Image recognition is employed in the license plate recognition systems that check and control the movement of vehicles in areas that allow access to such vehicles only. Image recognition technology is also used in security cameras to monitor against any suspicious movement or intrusion to avoid security threats or breaches within the cameras’ surveillance.
2. Autonomous Vehicles
Autonomous vehicle development is based on image recognition tools more than anything else. These vehicles incorporate cameras and sensors to photograph the environment and utilize AI techniques to identify objects, including pedestrians, vehicles, and traffic signs on the road. This capability is very vital for the kind of navigation self-driving cars are supposed to do.
Automated image recognition also improves driver assistance systems in normal automobiles. For instance, such features as lane departure warning, ACC, AEB, etc., are made possible by analyzing the video stream that the car receives from the surrounding environment.
3. Agriculture
Image recognition is changing farming as traditionally known and practiced through precision agriculture. The new approach involves the use of image recognition technologies to evaluate crop health, pests, and diseases. Farmers can use drones with cameras to photograph fields and capture aerial images. A closed-circuit television monitoring can facilitate crop review and the identification of the state of a given crop and any problem that may call for reduced yield.
4. Manufacturing and Quality Control
In manufacturing, image recognition is widely used in quality checks and defect identification. Robotic systems can check the bodies of integrated products undergoing production in assembly lines for imperfections such as cracks, misalignment, or compromised components. That ensures that only quality products reach the market, minimizing recall. That goes a long way in maintaining your brand's reputation.
It is also useful in automating the sortation and packaging processes of the products based on their images. It is possible to recognize shapes, sizes, and colors. That makes it easy for the system to sort products in the right category to enhance production while minimizing possible mistakes.
5. Entertainment and Media
The entertainment and media sectors also incorporate image recognition technologies in their market. In video streaming services, image recognition is employed to essentially pre-tag scenes and episodes to enable users to find particular episodes. For instance, in a video context, artificial intelligence can detect actors, sets, assets, and emotions from the video frames, enriching the content recommendation system and personalization.
Besides, image recognition is also useful in augmented reality and virtual reality areas. These technologies involve identifying physical objects and spaces to generate such experiences. For instance, in AR gaming, a gaming system or gaming console can perceive locations and objects and place digital content on them to produce a tangible gaming experience.
6. Banking and Finance
In the banking and finance sector, an image recognition app can solve issues surrounding forgery and identity confirmation. For example, facial recognition technology is used in mobile banking apps to authenticate users, providing an additional layer of security. It is also employed in ATMs to protect the machines from being accessed by unauthorized people and in payment systems for identification purposes.
Furthermore, image recognition is useful in document verification to recognize and validate checks, passports, and ID documents. That has a knock-off effect of doing away with manual data entry, fast-tracking the process.
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Source: Story.KISSPR.com
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