What are the Different Applications of Computer Vision — A subset of Artificial Intelligence?
When we randomly browse the Internet, we see a lot of information being conveyed through videos and images. Every search in Google has a good deal of images associated with them. Since there are a huge number of images and videos present all over the Internet, it becomes obvious that gaining a good intuition and general patterns from them could be really beneficial to users and the society. Furthermore, it would really add value if computers are able to understand whatever they see and generate information from them. There is a sub-field of artificial intelligence called Computer Vision that generates meaning from videos and images and gives a numerical representation of them so that they could be used for Machine Learning purposes.
What is Computer Vision?
Computer Vision is a field of Artificial Intelligence that gives computers the ability to understand digital images and videos, and allows them to make suggestions to the users for many purposes. In other words, it gives the ability to computers to be able to generate insights from the images and videos that are available in numerous formats. With the aid of Computer Vision, there are many tasks that are automated such as Self-driving cars, Image Recognition, and many others. Let us go over some of the applications of Computer Vision.
The following are some of the exciting applications of Computer Vision.
- Stroke Recognition
In different sports, there are objectives to score goals or win matches. In the game of baseball, players are required to hit a ball, and a score is given according to the distance that a ball travels along with other conditions. Normally, a lot of coaches and analyzers of the game would understand the match and give feedback to players so that they improve over time. On the contrary, Computer Vision algorithms predict the stroke and help the team members and players to get to know the mistakes and help them make wise decisions in the future. Therefore, this is one of the interesting applications of Computer Vision.
2. COVID Detection
There are many X-Ray images generated from patients all over the world. Researchers and Doctors label the images as to whether the patients test positive for COVID or not. Computer Vision is used in this scenario where it would detect the chances of a person to suffer from COVID based on annotated examples given by the Doctors and Researchers. Therefore, Computer Vision could be used in this scenario where the task could be automated so that Doctors could use time elsewhere. Moreover, the time it takes to diagnose a patient for this disease could be reduced significantly. Hence, Computer Vision could be used in Diagnosing the condition of a patient.
3. Mask Detection
During the time where there is a spread of diseases, it is crucial that precautions are taken by people to impede the spread. One thing that they could do is to wear masks when going to public spaces and gatherings. In some places such as restaurants, it is mandatory to wear masks. However, it is difficult to monitor every individual as to whether they are wearing masks or not. Computer Vision is used in this scenario to help track individuals and help the people know whether they are wearing masks or not.
4. Crop Monitoring
Generally, satellite images are generated to track the conditions of crops over different fields. Once those images are generated, it is the duty of the management to understand the scenario and take action based on the condition of the crops. Basically, the work is being done by analysts who are experienced in the field of crop management. Nevertheless, analysts are being replaced by data scientists and machine learning engineers. Generally, when there are annotated examples of satellite images, those images could be fed to the deep learning algorithms, Convoluted Neural Networks to be specific, so that they would be able to let the analysts know the conditions of different crops for different regions respectively.
5. Defect Detection
Deep learning algorithms could be good at predicting the different defects that occur during the manufacture of items such as steel, concrete, and automobiles respectively. It would be really wise to train the deep learning algorithms to perform the task of detecting some defects present in some items such as steel and automobiles respectively. Once the labeled examples of defects are given the deep learning models, they would automatically detect certain defects, reducing the human effort. Furthermore, the time it takes to manufacture certain devices and tools would also reduce considerably with the use of these Computer Vision Algorithms respectively.
6. Retail Industries
There are many retail industries such as Amazon which are trying to automate the process of purchasing different items from stores. In fact, Amazon has recently started ‘Go’ which is a supermarket that would allow people to purchase items without staff. Computer Vision algorithms are used in the process of purchasing the items. For example, when a customer picks an item and adds to the basket, the item gets automatically picked and it registers the item for pay. The time it takes to purchase an item and walk from the store reduces significantly. Moreover, the stock could be monitored with this method in the process as well. Therefore, we see how it is being used in retail industries.
7. Self-Driving Cars
Computer Vision is being in Self-driving cars where the cars learn to drive on their own based on various traffic conditions, road maintenance, and other things that affect driving. The driving mode could change based on the condition of the road. During the night, Lidar sensors are being used to detect slopes and disturbances on the road. In the future, there would be a rapid increase in the number of self-driving cars, leading to better development in the space of technology.
Conclusion
All-in-all, we’ve seen how Computer Vision could revolutionize different industries with their predictions and understanding of images and videos. We’ve seen them being used in crop management and self-driving cars respectively. Hope you found this article helpful. Feel free to share your thoughts and feedback. Thanks!
