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Summary – Computer vision works much the same as human vision, except humans have a head start. In this article, we talk about the true definition of computer vision, how to benefit from computer vision technologies and the key examples of computer vision applications that are transforming industries. Scroll down for more information.

Computer vision enables computers to perceive, interpret, and understand information from digital images and videos. The real brains behind computer vision is deep learning, a form of ArtificiaI Intelligence which enables computer vision tasks like object detection, image generation, style transfer, and image captioning to occur. The Convolutional Neural Networks (CNNs) of deep learning have improved computer vision tasks and enabled its use and innovations across many industries.

Consider how many industries have benefited from computer vision technologies thanks to deep learning solutions.  Following are key examples of computer vision applications that are transforming industries:

The Automotive Industry

The automotive industry has been focused on the development of self-driving cars in recent years with the help of computer vision techniques. Autonomous cars should be able to track all of the surrounding objects with cameras and react according to what is happening in their driving environments. The algorithms have laid out the groundwork for driving scene perception, path planning, behavior arbitration, and motion control in autonomous vehicles.

Disaster Relief and Emergency Situations

Natural disasters like hurricanes, earthquakes, wildfires or floods require a quick assessment of the situation in terms of damage to the environment and infrastructure of the area so that proper action can be taken, such as mapping of high vulnerability areas and response to numerous natural disasters scenarios.


AI driven computer vision can be used to enhance agriculture by increasing yields as it informs farmers about efficient growth methods, crop health and quality, pest infestation and soil conditions. Image classification techniques are currently being used to automate quality control of crops by grading and sorting them based on their physical parameters and properties.

Meanwhile, multispectral and hyperspectral aerial imagery provided by drones capture detailed information about soil and crop conditions to help monitor stress and disease in the farming area.


There are multiple examples of computer vision applications in healthcare that not only enable better and more efficient care, but that also are proving to be life-saving for many patients. It allows medical professionals to monitor conditions and diseases and make diagnoses which will guide how doctors prescribe medications and give out treatment, as well as detect fatal illnesses. These applications also improve medical processes as it reduces the time doctors use analyzing medical images and gives them more time for consultation with patients.


Computer vision applications can be integrated into security cameras in airports, parking garages, and other public and private facilities in order to harvest real time information from video feeds. Face recognition technologies are also being widely used for authentication purposes in various industries.

Retail and Inventory Management

Retail stores can use computer vision technologies to track customer activity that would provide valuable insights into consumer behavior, as well as information about the effectiveness of merchandise placement strategies that could enhance customer traffic. Shelves with intelligent computer vision applications can accurately monitor and track inventory in real time, saving operational costs and allowing retailers to focus more on customer experience.


Banks and other financial institutions have already started to implement computer vision. Some institutions allow their customers to open accounts using facial recognition for verification. This approach has proven to be less time consuming than traditional pen and paper methods.

Image processing can also be used for electronic deposits as the customer submits an image of the front and the back of a check and the transaction is then analyzed and completed.


Good advertising can help consumers discover products and services through their visual properties, tracking and visualizing the emotional reactions. This can help personalize product placement and aid in marketing strategies. Moreover, products can be discovered with image generated properties through queries that use images as inputs, since textual descriptions of items can often be difficult to explain.


AI powered cameras can help teachers, instructors and educators monitor their students’ behavior in order to improve classroom interactions and enhance the learning experience. Computer vision solutions can bring important insights to education that can greatly improve teaching methods and personalized learning.

Waste Management

Advancements in computer vision have powered AI-based waste recognition technologies. Waste monitoring through object detection can be used to automatically sort waste in bins, trucks, and facilities. This optimizes the waste management and recycling process. Smart bins have also been developed to accept recyclable materials and reject organic or undesired waste in an automated fashion.

The improvements deep learning has provided to the field of computer vision has greatly impacted various industries and society as a whole. AI driven applications in image processing are currently improving business decisions, optimizing processes, and creating safer services and transactions between people and communities.

While there are many examples of computer vision applications impacting and disrupting industries, there are still many challenges to implement this innovative technology.  The data collection process for these types of applications can be expensive and time consuming.

Additionally, privacy and security concerns still loom around the rise of computer vision applications. Yet, with proper controls and measures, there is no denying that deep learning -based computer vision is changing the way businesses and institutions in a variety of industries operate in modern society and it’s just the tip of the iceberg.

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