Gone are the days when computer vision was used for surveillance purposes only.

Computer vision is an artificial intelligence application that replicates the intricacy of the human vision system using neural networks. It helps users to analyze high-dimensional visual data and produce meaningful insights. Sub-disciplines of computer vision include image restoration, object recognition, and anomaly detection. Today, computer vision systems have been developed to an extent that they can offer higher precision of information than humans. Besides, its use cases are no longer limited to facial recognition for personal password login and tracking for video surveillance – but have penetrated several industries.

The below-mentioned examples show use of computer vision in top industrial niches and sectors.

E-commerce: When a new product is added into an e-commerce site catalogue, its features are automatically extracted using computer vision algorithms.  This automates the process of labeling every new item that a store wants to add, allowing products to go up on the virtual shelves and into consumers' attention quicker.

Autonomous Vehicles: 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. Insight inputs from such data analysis helps in real-time prediction of accidents, warn the driver, and in some cases, also alert the driver if he is about to sleep. E.g., Waymo cars are equipped with sensors and software that can detect 360 degrees of movements of pedestrians, cyclists, vehicles, road work and other objects from up to three football fields away.

Manufacturing: Many companies are employing computer visions to keep real-time track of their machines and assembly operations. E.g., ZDT software made by FANUC is a preventive maintenance software designed to collect images from camera attached to robots. Then this data gets processed to provide trouble diagnosis and detect any potential problems.

Computer vision technologies also help in eliminating risks for the workers by precisely identifying cracks, corrosion, leaks, and other anomalies in the machines. Besides, packaging and product quality are monitored, and defective products are also reduced with computer vision.

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.

Retail: 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.

Digital Marketing: Brands can leverage computer vision tools to sort and analyze millions of online images, to figure out how to bypass traditional demographic research and still target marketing to the right online audience. Brands can further use computer vision to ensure proper placing of campaign ads that can help in attracting its audience.

BFSI: Computer vision has a multitude of usability in banking, insurance and other financial domains. For instance, in 2019, Caixabank allowed their clients to withdraw money via ATMs using face recognition. The ATM can recognize 16,000 facial points on an image to verify the identity of a person. In insurance claim requests, computer vision tools can assist in the process of inspecting damaged property, documents verifications and others.