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23 September 2021

Industrial Applications of Computer Vision and Why it Matters?

Applications of Computer Vision

Applications of Computer Vision in industries can affect how we interact with everything from digital devices to social media. It can even change how we see ourselves.

Today, we will explore what computer vision is, computer vision applications and why it matters for people and businesses alike. We will also touch on some lesser-known applications that are changing how we live our lives, like driverless cars or medical imaging systems in hospitals. So, if you’re curious, keep reading!

What is Computer Vision?

Computer Vision (CV) is a process that uses machine learning to analyze, understand and respond to digital images or videos.

Generally, we train neural computer vision networks by feeding selected pictures and images as cues. Thus, it enables them to recognize objects or people without mistakes. Moreover, computer vision technology can classify those same items based upon their properties like size, shape, etc., among many others.

Curious minds often dive into this debate whether computer vision can beat human vision ever.

Interestingly, human vision is still superior to computer vision. However, the processing methodology in human vision vs. computer vision is very different.

The human brain processes visual information by extracting semantically meaningful features such as line segments, boundaries, shape, etc. Unfortunately, computers aren’t smart enough to detect these features. Therefore, computer vision technology has its limitations and cannot process information like human vision.

Computer vision process information by image understanding and use it to make predictions or decisions.

Read more: Are AR, VR, and XR the Future of Corporate Training?

As computer vision artificial intelligence becomes more prevalent in our daily lives, more people are tapping into the solutions to achieve better outcomes in business.

The 2020 McKinsey Global Survey on artificial intelligence reveals that 50% of companies have adopted AI in at least one business function. In addition, businesses reported that they use the most significant computer vision application toward product or service development.

Applications of Computer Vision:  How to detect and track an object with computer vision?

Computer-vision systems can detect and track objects in many ways, including:

Applications of Computer Vision

Image Classification Vs. Object Detection:

The type of image is recognized,  whether it’s a person’s face or landscape objects. We also identify and block inappropriate content on social media platforms like Facebook, using image classification.

For example, you might not want someone sharing your pictures with everyone without permission!

Computer vision object detection identifies a particular trait in an image like X-rays with fractures that can be used to create computer vision systems.

Object Recognition:

 Object Recognition is an integral part of Natural Language Processing. When we talk about images, object recognition refers to the identification and segmentation process for individual objects in a scene—like pizza on the cluttered tabletop!

Contact us to get an object recognition demo!

Edge Detection:

A popular way for algorithms such as those used with image recognition software (iCam) is often designed only to analyze images pixelated enough where there isn’t much detail present.

It helps researchers determine what features make up an edge versus other areas within its given context.

Object identification:

Object identification is the recognition of individual examples, like identifying a person or car.

Object segmentation:

Object segmentation is the process of determining which pixels in an image belong to specific objects.

Object tracking:

We can recognize an object in a video sequence. We can quickly track throughout the whole clip.

In the future, we will rely on computer-vision AI to interact with our devices. If you are using emerging technology, then there are substantial opportunities ahead.

Applications of Computer Vision:  How is computer vision transforming commerce?

The computer vision market is expanding at a projected rate of 45.64% CAGR per year.  As a result, global markets for this technology will reach 144.46 billion by 2028.

The computer vision revolution is already making its way into the modern workplace.

Read more: How These 6 Industries are Benefiting From the Commercial Use of Drones

A report from Grand View Research suggests that as tactics become more advanced and technologies such as IP cameras decrease in price over time, companies can access these capabilities at an affordable rate too!

We have documented a few computer vision use cases in industries like energy, transportation, and healthcare.

Computer Vision and Machine Learning in Energy:

Imagine a world where you could see cracks in the power line before they became an issue. It is possible with computer vision data, which uses images captured by cameras or other sensors to detect signs of wear on equipment and provides early warning for maintenance issues that may arise down the road–such as leaks from pipelines underwater bowls at home!

The need for safety, efficiency, and regulatory compliance has led to a broad range of use cases across the energy industry.

Forward-thinking organizations are already leveraging AI and computer vision to monitor equipment for signs of wear or leakages and safely inspect linear assets such as power lines or pipelines in correlation to multiple models from different cameras.

Computer vision detects cracks on an object (such as warning lights). Other sensors like accelerometers detect movement which helps pinpoint any potential issues before it becomes problematic.

Industries use computer vision machine learning for many other purposes, including identifying authorized personnel badges in restricted areas or even providing alerts when an individual has crossed a designated safety threshold.

Applications of Computer Vision

Image Processing and Computer Vision in Transportation Applications:

Computer vision programs can help transportation and logistics professionals to identify problems in their operations with greater accuracy than humans. For example, we use computer vision to count pallets or alert you if any are damaged before a warehouse clerk notices the incident.

Imagine warehouses without any damage to their goods. Imagine being able to see what type of vehicle was bringing in which pile or how much weight there was on each pallet before loading it onto your truck, so you’ll never be caught off guard by an overweight shipment again! All this is possible due to the application of computer vision.

Computational vision can also help transportation companies decrease costs through cuts for inventory counts and routing decisions. The technology will always let them know if something’s amiss somewhere along that supply chain network route from a supplier.

Some companies have found that drones are an excellent tool for ensuring safety and efficiency among their transportation fleets.

Modern railway companies can use CV-enabled aerial vehicles (drones) to conduct inspections along thousands of miles of track.

It reduces costly fieldwork with hazardous results. It also allows human inspectors to fix problems virtually by eliminating on-site inspections,  manual labor, and efforts.

Hence, they’re able to make adjustments if necessary before heading out again without too much difficulty.

Best Computer Vision in Healthcare:

Computer Vision AI can have an immense impact on medical diagnostics. Companies like Google are already exploring it for risk assessment or early detection.

The potential benefits of using computer vision, machine learning, and deep learning in the healthcare sector are significant.

More accurately than ever before, identifying at-risk individuals has been heavily explored by researchers over recent years with computer vision and machine learning. The results are optimistic!

Computer vision and AI-powered tools in medical diagnostics can help determine the risk of disease early on.

There are several other applications of computer vision in healthcare. For example, we can use visual models to track handwashing among medical staff, providing reminders if they miss any step.

Computer vision network also allows automatic processing of documents, reducing administrative burdens and lowering costs of care.

However, these projects come with additional layers when considering misdiagnosis rates due to human error during machine learning training processes. So, the risks associated with misdiagnosis mean that we need to take more precautions about using machine learning algorithms for treating people.

Authorities should consider misdiagnosis rates and human errors if they want AI, CV, and ML to revolutionize how people receive medical treatments to live healthy lives in the future.


The potential of computer vision, machine learning, and deep learning are limitless. For example, your entire personality can be read by a machine through the application of computer vision alone, from the way you dress to your facial expressions when speaking with someone.

Computer Vision (CV) refers to the science of using computers for image processing. We can use CV in many fields, from robotics and self-driving cars to medical imaging such as X-rays or CT scans

Computer Vision and deep reinforcement learning have changed how we interact with our computers and digital devices. Big Tech companies are leading pioneers in the application of computer vision in businesses to provide services faster.

For example, companies such as Google and Facebook use computer vision algorithms to analyze advertisements on their platforms; it also helps object recognition or facial detection.

Computer vision meets machine learning in the industrial landscapes and changes the way we see and interact with technology. With this new insight, let’s rethink how we design our products and services to make them more efficient for people.

For years to come, groundbreaking innovations like computer vision and deep learning algorithms will continue to revolutionize how businesses operate across all sectors!