If you have watched an
image trying to load on a social media app, you will usually see a description
in its place. It will be a descriptor of things in the image such as a field of
flowers or a child holding a balloon, and so on. This is computer vision. Facial
recognition that is again the rage on social media apps and other software is
another aspect of computer vision. Read on to find out more about this
Computer vision or computer vision AI is a field of
artificial intelligence where computers are trained to identify, interpret, and
process images in videos and images in a similar way as humans do. Significant
leaps in artificial intelligence, especially in the aspects of neural networks
and deep learning have enabled computer
vision AI to take great strides in recent years.
Computer vision uses
Computer vision AI is used by multiple industries ranging from retail to the
military to healthcare and more. Here is a quick overview of how computer vision AI is used by different
Retailers are using computer vision in a
number of ways. They use it to increase loss prevention, uncover
out-of-stock shelves, enhance shopping experiences, using self-checkout
counters, and so on.
Here computer vision is being used to detect
and identify manufacturing defects in real-time. During the assembly line
process, the computer vision AI
can detect even the smallest of defects on a product.
Computer vision AI is being used to screen and
thoroughly examine images from X-rays, CAT scans, and MRIs with a high
rate of accuracy.
In the insurance industry, computer vision AI is being used
to reduce fraud by enabling accurate and consistent assessments of
- Defence and security:
Computer vision is used extensively in the
defence and security sectors. For example, casinos and banks use computer vision AI to analyse
images from their security cameras. It is also used extensively to improve
surveillance of power plants, embassies, hospitals, stadiums, railroads,
and so on. It is also used to enhance the inspection of cargo at ports. Computer vision AI is also being
used in reconnaissance missions by the military, as well as to quickly
identify enemies, and to automate machine and vehicular movements.
Computer vision has a tremendous impact on the
agricultural industry. It allows farmers to detect diseases and pests
quickly and on time. Early diagnosis prevents unnecessary loss and ensures
product quality. Computer vision-enhanced robots monitor farms for weeds
and spray herbicides to prevent or stop weeds from taking over.
Furthermore, computer vision AI
is used to help in the sorting of vegetables, fruits, and flowers by
weight, size, quality, and other identifying markers.
- Autonomous vehicles:
Computer vision AI plays an important role in
self-driving vehicles. Here it plays a major role in perceiving and
identifying the environment around the vehicle, thus allowing for
- Augmented reality:
Here it detects objects in real-time and uses
the information it has processed to place virtual objects within the same
- Facial recognition:
This is where computer vision AI matches the images of people to their
identities. This technology is not just restricted to security and defence
sectors but is also used by social media apps and biometric authentication
on smartphones, and so on.
Aspects of computer vision AI
There are four main
aspects of computer vision AI also
called the four eyes of computer vision. These are object recognition, video
recognition, image recognition, and machine vision.
This is where computer vision
is used to identify people, places, actions, and objects in an image.
This has a similar process to
image recognition but it also allots a class label to which the image belongs.
For example, in self-driving cars, computer vision recognises distinctions in
traffic lights, and can also differentiate between a pedestrian and a lamppost.
This is where computer vision AI analyses video clips
and compares those clips to a database of other content to ascertain if there
is a match.
This is a hybrid of hardware
and software. The technology here can conduct inspections and provide guidance
to robots via visual feedback.
How does it work?
Here is a basic outline
of how a computer vision model is built:
- A dataset is created incorporating annotated images.
The annotation can be comprised of the image category, pixel-wise
segmentation of the objects present, or pairs of classes and bounding
- From each image, features that are relevant to the
task are extracted. For example, in images, for a task to recognise
people, the computer vision AI
will recognise features based on facial features.
- Then a deep learning model is trained based on the
features that are isolated in the dataset. This means that the AI is fed
many more images to learn how to solve the required task.
- Thereafter, the computer vision model is evaluated by using
images that were not used during the training phase.
Limitations of computer vision
for specialists: Companies need a team of highly skilled specialists that can
build and use computer vision AI.
for regular monitoring: To prevent technical glitches, companies need a
dedicated team to constantly monitor and evaluate the system
replacement for human intelligence and vision: Computer vision AI has come a long way but it is not a replacement
for human intelligence and human vision. Machines do not understand the
complexities and intricacies of the world around us the way humans can.
The bottom line
Computer vision AI is an exciting technology that has taken the field of
artificial intelligence to new heights. The world will surely see much more
advancements and innovations in this field in the future and it will continue
to revolutionise our world as we know it.