Computer vision is a subfield of artificial intelligence (AI) that aids computers and systems to extract useful information from digital photos, videos, and other visual inputs. Today, it has numerous applications. However, before diving deep into the same, first, there is a need to understand a bit about its timeline and how the technology evolved over the years. 

For nearly 60 years, scientists and engineers have attempted to find ways for machines to comprehend and analyse visual input. For example, in 1959, neurophysiologists presented a cat with a series of images in order to see if it could correlate with a response in the cat's brain. As a result, the first computer image scanning technology was created about the same time, allowing computers to digitise and acquire images. 

• 1963: This was the time when computers got the ability to transform two-dimensional images into three-dimensional forms. 
• 1974: Optical character recognition (OCR) technology, which can recognise text printed in any font or typeface, was introduced this year.  
• 1982: David Marr, a neuroscientist, discovered that vision is hierarchical and developed methods for systems to detect edges, corners, curves, and other basic shapes. 
• 2000: The focus in this field shifted to object detection, and real-time face recognition applications emerged the next year. 
• 2010: The ImageNet data set was made public. It comprised millions of annotated photos from a thousand different object classes and served as the basis for today's CNNs and deep learning models. 
• 2012: A team from the University of Toronto successfully entered a CNN into an image recognition contest. AlexNet - the model from the team significantly reduced the error rate for image recognition which has now dropped to a significant level. 

With an understanding of the technology and its background, let's understand the top five applications of computer vision: 

Self-driving cars 

Autonomous vehicles are no more a fictional story, and multiple startups are there in this domain with successfully deploying AV on Indian roads. Thousands of engineers and developers across companies are currently testing and enhancing the efficiency and safety of self-driving cars all over the world. With the help of computer vision, it is easy to recognise and classify things (such as road signs, traffic signals or pedestrian movement), construct 3D maps, and estimate motion, and it was crucial in the development of autonomous vehicles. 

To be precise, computer vision techniques such as pattern recognition, feature extraction, object tracking, and 3D vision are used by researchers working on ADAS technology to produce real-time algorithms that aid driving activities. 

Healthcare 

Computer vision is also commonly used to analyse CT and MRI data. It is the key to improving patient outcomes, from building AI systems to evaluate radiological images with a higher level of accuracy than human doctors (while reducing disease detection time) to deep learning algorithms to boost the resolution of MRI scans. In addition, computer vision can aid clinicians in detecting tumours, internal bleeding, clogged blood arteries, and other life-threatening conditions by analysing CT and MRI data.  

From cancer detection to blood loss measurement, computer vision is used in our day-to-day life, with dentists detecting dental problems within minutes and providing an AI-assisted report. 

Manufacturing 

The manufacturing industry has already embraced a wide range of automation systems centred on computer vision. It aids in the automation of quality control, reducing safety risks, and developing production efficiency. With the help of camera-based systems, one can collect real-time data, evaluate it, and compare the results to a set of quality standards (for the purpose of defect inspection) using computer vision and machine learning techniques. 

Moreover, computer vision helps to maintain packaging standards by generating 3D modelling designs, guiding robots and human employees, identifying and tracking product components, and generating 3D modelling designs. 

Agriculture 

Artificial intelligence, including computer vision, has significantly contributed to the agricultural sector in areas such as crop and yield monitoring, automated harvesting, livestock health monitoring, weather analytics, and plant disease identification. Take, for example, early diagnosis of insect pests allows farmers to take the necessary precautions to protect their crops and limit the damage. In addition, crop surveillance systems that use cameras may identify, classify, and count insects that pose a hazard to crops. 

Recently, the Chandigarh University Department of Research and Development has created an AI-based Mobile Application based on the CV to detect crop diseases. As a result, the team's method helps farmers in the early detection of disease and take appropriate detection to secure the yield. 

Retail 

Retailers may capture significant amounts of visual data with cameras deployed in their stores, which can help them build a better customer and employee experience. In addition, the emergence of computer vision systems to process this data has made the digital transformation of the actual world a lot more feasible. 

One of the real-world applications includes computer vision systems to capture image data and conduct an inventory scan by tracking objects on shelves at millisecond intervals. The system then sends out real-time information about stockouts and sales, as well as assists employees with inventory management.