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Application of Computer Vision in different Industries

Only in recent years did the world witness a significant leap in technology that has put computer vision on the priority list of many industries.

Computer vision technology is transforming the business world with its capability to understand the content of digital images and videos. It enables machines to precisely identify and classify images based on deep learning capabilities. Computer vision can be used across different industry verticals to enhance productivity, efficiency, customer experience, reduce operating costs, minimize defects, and improve security.

The application of computer vision technology is very versatile and can be adapted to many industries in very different ways. Some use cases happen behind the scenes, while others are more visible. Most likely, you have already used products or services enhanced by the innovation.

From our research, we’ve found that many of the use cases of computer vision fall into the following clusters:

Computer Vision in Automotive

Computer vision systems in autonomous vehicles like self-driving cars continuously process visual data from road signs to seeing vehicles and pedestrians on the road and then determine what action to take.

Applications of computer vision in ADAS are prominent. Computer vision through vision-based ADAS (Camera-based), RADAR, and LIDAR technologies are paving the way for automotive companies towards fully automated self-driving cars. Different systems inside a car perform different tasks like camera-based ADAS provides visual representation, Radar works in case of low visibility, and LIDAR provides 3D representation of vehicle’s surroundings with object recognition. However, all these applications of ADAS are possible with computer vision technologies, which together provide a holistic solution in ADAS applications. This allows the driver to have a better awareness of his surroundings while driving, and at the same time have more control.

Computer Vision in Retail

Computer vision has made a squelch in the retail sector as well. Apart from ensuring security, spillage detection, and theft control, video analytics in retail with computer vision can help retailers concentrate on improving the customer’s shopping experience and optimizing operations.

Gourmet candy retailer Lolli & Pops uses computer vision-based facial recognition to identify loyalty members as they walk into the store. By sifting through their purchasing history and preferences, the system can make personalized product recommendations specific to each shopper. Doing so instills brand loyalty, and also converts occasional shoppers into regular customers. Amazon uses computer vision at Amazon Go stores, to allow customers to pay for goods without the need for a checkout.

Computer Vision in Manufacturing

Computer vision technologies in manufacturing units are very useful and they have unprecedented benefits to the business, like:

Predictive Maintenance: the role of computer vision comes into the picture, which analyzes every component of the production line and diagnoses even the minute defects in the system. Based on the detailed and precise investigation, computer vision systems can predict any chances of future failure in the system, notifies technical team to fix that cause, and ensures no downtime in the production.

Identifying Defects: Inspection for the defects in the industrial setup can be very risky, tedious, costly and time-consuming, and sometimes it is next to impossible to detect any defects in the machines manually. Computer vision technologies in such cases help in eliminating risks for the workers and works precisely to identify cracks, corrosion, leaks and other anomalies in the machines.

Computer vision in Security and Surveillance

The security and surveillance industry was one of the first to implement computer vision technologies. It is computer vision that has greatly enhanced video surveillance and intelligent video analytics techniques and accuracy. The amount of data produced by video surveillance systems is calculated by the number, form, and resolution of video cameras used in a given project.The huge amount of video feeds are of no use, unless some critical information can be generated. Computer vision has made this possible with use of AI and ML in video analytics.

Computer vision capabilities in security and surveillance are based on video management software and its hardware, third party devices (like sensors, alarms, access control devices), network, interfaces, signal processing capabilities, pattern and object recognition, etc.

Computer vision in Healthcare

In healthcare, computer vision has the potential to bring in some real value. While computers won’t completely replace healthcare personnel, there is a good possibility to complement routine diagnostics that require a lot of time and expertise of human physicians but don’t contribute significantly to the final diagnosis. This way computers serve as a helping tool for the healthcare personnel.

One of the main challenges the healthcare system is experiencing is the amount of data that is being produced by patients. It’s estimated that healthcare related data is tripled every year. Today, we as patients rely on the knowledge bank of medical personnel to analyze all that data and produce a correct diagnosis. This can be difficult at times.

Microsoft’s project InnerEye is working on solving parts of that problem by developing a tool that uses AI to analyze three-dimensional radiological images. The technology potentially can make the process 40 times quicker and suggest the most effective treatments.

Computer vision in agriculture

With the help of drones, farmers can spot crop diseases, predict crop yields, and, overall, automate the time-consuming processes on manual field inspection. It can also identify weeds so that herbicides can be sprayed directly on them instead of on the crops. During CES 2019, John Deere featured a semi-autonomous combine harvester that uses artificial intelligence and computer vision to analyze grain quality as it gets harvested and to find the optimal route through the crops. Companies like Cainthus uses predictive imaging analysis to monitor the health and well-being of crops and livestock.

Computer vision applications provide valuable information about the irrigation management water balance. A vision-based system can process multi-spectral images taken by unmanned aerial vehicles (UAVs) and obtain the vegetation index (VI) to provide decision support for irrigation management.

Conclusion

It’s all about the usage of computer vision applications increased in various industries. Some of the industries have adopted the technology faster than others. How much may be computer vision technology increasing it continues to rely on the human effort to monitor, interpret, analyze, control, and decision-making. In addition to helping the automation, computer vision allows stores to operate with minimal human intervention.

As machines and humans continue to collaborate, the human workforce will be freed up to emphasize on higher-value errands because the tools will computerize the process that relies on image recognition.