Since its birth in 1950-56, AI has brought various scientific innovations and advancements that have not just improved our work efficiency, but also given us a glimpse of a cutting-edge future. One of its breakthroughs is computer vision in AI. It is an AI technology that uses computers and machines to analyse and perceive different images and videos instantly. It significantly helps to streamline extravagant industrial tasks.
Imagine you are seeing a particular object. When you capture it with your eyes, its imagery signal hits the retina, which is then transferred to the brain through the optical nerve. If you have seen the object before, you can identify it instantly. Similarly, we can feed information about different objects to computer vision AI, and it can identify them according to their size, shape, and colour faster than the human eye.
This blog comprehensively focuses on computer vision in AI, its key features, history, examples, its importance, how it differs from human vision, how it works, and its applications in the industrial sector.
What is Computer Vision in AI?
There was a time when humans had to do key tasks, such as project inspections, infrastructure monitoring, and defect detection. With its introduction into the market, computer vision AI surpassed not only its speed but also objectivity, continuity, accuracy, and scalability.
AI Computer Vision Definition
AI computer vision is a field of Artificial Intelligence that incorporates different computational algorithms, such as Deep Learning, CNN, and Machine Learning, to see, analyse, understand, and process images, videos, and other visual inputs to derive important information and make decisions. It is similar to the human eye and significantly increases accuracy and precision.
Key Features of AI Computer Vision
Here are the key features of AI computer vision:
- Visual Perception: AI computer vision efficiently perceives visual data and information using cameras, sensors, and digital input devices.
- Image Processing: After it perceives the image, it processes it by cropping, scaling, and adjusting its brightness and contrast.
- Pattern Detection: It also detects the patterns of images by reading their shapes, sizes, and textures.
- Pattern Classification: It interprets the objects and compares them by known patterns. Then, it classifies them according to their physical characteristics.
- Optical Character Recognition: AI computer vision also helps scan images and written documents into machine-readable formats.
History of Computer Vision in AI
The history of AI computer vision dates back to the invention of Artificial Intelligence (AI) in the 1960s. After AI, scientists began to think about teaching computers and machines to read, understand, and interpret visual data.
In June 1963, Larry Robers, also known as the father of computer vision, presented his thesis on Machine Perception of Three-Dimensional Solids at MIT. This thesis dealt with the understanding of 3D visual objects from 2D images. In 1966, the Summer Vision Project took place at MIT, which focused on identifying different segments of a picture and classifying them.
In 1967, at the IEEE Computer Conference, Woodrow W, Bledsoe, and I. Kanter presented their thesis about face recognition using a combination of edge detection and feature matching. In 1973, Michael A. Fisher and Robert A. Elschlager presented their paper on Representation and Matching of Pictorial Structures, which focused on finding objects in pictures based on the information provided.
In 1986, Horn and Brooks published a paper on recovering 3D shapes from images using shading information. Soon after, in 1987, Deriche’s edge detector was invented, a recursive filter designed to find edges in noisy images.
Nowadays, after many years of evolution, AI computer vision has brought various gifts, such as OpenPose, Mask R-CNN, YOLO V3, ViT, and ChatGPT. These have significantly made our lives convenient and fast.
Examples of Computer Vision in AI
Here are the different examples of computer vision in AI. These will help you understand this technology efficiently.
⭕Example 1: Facial Recognition
AI computer vision is widely used for facial recognition in smartphones. Whenever a user uses his face to lock, this technology analyses and detects his features to unlock the phone. It significantly enables efficient security in real time.
⭕Example 2: Application in Healthcare
In healthcare, AI computer vision systems are used in medical scans, such as CT, X-ray, and MRI scans. This helps specialists efficiently detect diseases like cancer, tumours, and fractures. It also enhances accuracy and eliminates human error.
⭕Example 3: Social Media Content Moderation
AI computer vision is also used in social media for content moderation. For example, Facebook and YouTube use this technology to detect inappropriate or copyrighted content and automatically remove offensive images and videos.
AI Computer Vision Market Size
With the increasing use of AI in our daily lives, the AI computer vision market is also growing gradually.
According to Statista, the AI computer vision market is expected to reach US $25.80bn in 2024. The market size is expected to grow annually at the rate of 10.50%, resulting in a volume of US $46.98bn by 2030. The United States will have the largest AI computer vision market compared to other countries.
As you can see from this chart, computer vision in AI is predominantly used for facial, image, and speech recognition.
However, according to the Market.us report, the global AI computer vision market is expected to reach approximately US $58.90bn by 2033.
Importance of Computer Vision in AI
As our eyes see and understand the world, so do the machines and computers that have started to analyse and understand the world using AI computer vision. They have enabled us to process more volumes of data much faster. This is not just one benefit of computer vision; here are the different benefits of this technology:
- It Improves Accuracy
Do you know? AI computer vision has incredibly increased the accuracy of operations from 50% accuracy to 99%. The accuracy levels in the bulk object identification process have significantly increased with the new algorithms and hardware in AI computer vision.
- It Increases Productivity
Another benefit of AI computer vision is that it has immensely increased work productivity. According to a report by AEM, US crop production has increased from 4% to 6% due to AI computer vision. This provides a golden opportunity for agriculture marketers and financiers.
- It Streamlines Quality Control Process
AI computer vision has increased productivity and skyrocketed the quality control process. According to a case study by Softengi, AI computer vision has detected bacteria in medical equipment with an accuracy of 99%. It will help in reducing different diseases and anomalies.
- It Helps to Save Costs
Do you know? According to a 2022 McKinsey Report, the use of AI-powered machines and computers has increased from 1.9 to 3.9 from 2018 to 2022. The main reason behind this was that it helped them to reduce operation costs significantly.
- It Contributes to Advanced Security
AI computer vision also significantly contributes to advanced security systems. It has enabled people to beat cyber threats using AI-generated cyber security measures. According to Statista, the market for AI cybersecurity is projected to reach US$134 bn by 2030.
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Book a ConsultationDifference Between Human Vision and Computer Vision
There might be various similarities between human vision and computer vision, but there are also significant differences. Human vision is a complex process that can be subjective and prone to bias, whereas computer vision equips computers and machines to have human vision powers. Here are the key differences between human vision and computer vision:
| Human Vision | Computer Vision |
|---|---|
| It relies on the eyes and brain to see, understand, and interpret visual information. | It relies on AI algorithms and neural networks to see and process visual information. |
| It doesn’t require training as it can extract meaning from incomplete or distorted visual data. | It requires explicit training of AI computer vision models to see and interpret visual data. |
| It has certain limitations concerning speed and the volume of visual data. | It can process significant amounts of images and videos at high speed. |
| It can easily adapt to new environments and changes. | It requires additional data and training to adapt to new environments. |
| It can naturally detect depth, links, and movements without additional tools. | It requires a stereo camera and LiDAR technologies for depth perception. |
How Computer Vision Works in AI?
Our eyes and brains can naturally identify objects using past experiences and knowledge about those objects, but this is not the case with computers and machines. To make AI computer vision work, machine learning models, such as Deep Learning and Conventional Neural Networks (CNN) come into the scene. When a large amount of visual data is fed to the computer using these models, the computer can “look” at the data and identify images according to their distinctions.
Deep learning utilises these algorithms to see, process, and analyse a large amount of visual information and recognise them from their patterns. However, CNNs excel at analysing image data. They break down the images into their smallest units, pixels, and label them efficiently. Then, they use different types of mathematical convolutions to detect patterns, identify objects, and make decisions. After that, they start to identify their edges, shapes, surface, lawyers, and depth.
Machines and computers continuously perform this detection process, making mistakes and refining them until they become precise and flawless.
10 Applications of Computer Vision in AI
Whether you know it or not, AI computer vision has become an integral part of our daily lives. From face recognition to security cameras and self-driving cars, AI computer vision makes our lives safe, convenient, and luxurious. Whenever we look at different industries, we see how AI computer vision is incorporated and how it enhances their key functions and operations.
According to Statista’s chart, this is the market size of the AI market vision by industry. These industries utilise AI computer vision to perform their key operations.
- AI Computer Vision in the Manufacturing Industry
Did you know that AI computer vision technology has increased labour productivity in the manufacturing industry by 12%? In manufacturing, AI computer vision is used for object feature detection, recognition, segmentation, and 3D modelling. It also monitors product quality, assembly lines, and the packaging process.
- AI Computer Vision in the Healthcare Sector
AI computer vision is also significantly transforming the healthcare industry. It is employed in various imaging technologies, such as CT, MRI, ultrasound, and X-rays. It has enabled physicians to detect and accurately diagnose fatal diseases efficiently. According to an HTR report, the global AI computer vision market in healthcare is projected to reach US56.1bn by 2034.
- AI Computer Vision in Security Operations
AI computer vision has also enhanced security and surveillance operations in domestic and commercial locations. It is used to perform intelligent perimeter monitoring, deep face detection, and face recognition with excellent precision and accuracy. It has exceeded unauthorized entry detection by 78%.
- AI Computer Vision in Traffic Control
Do you know drivers, on average, spend 54 hours stuck in traffic jams annually? This happens due to poor traffic control. AI computer vision in traffic manages traffic flow and ensures smooth transportation. The key activities in this sector are detecting traffic and signs, traffic flow analysis, parking management, number plate recognition, and detecting stolen vehicles.
- AI Computer Vision in the Energy Sector
In the energy sector, AI computer vision helps to increase operational efficiency, safety, and reliability. It also helps detect faulty objects, detect abnormal situations, and control personnel. According to InsightAceAnalytic, the energy sector’s AI computer vision market size is projected to reach US$45.0 by 2031.
- AI Computer Vision in the Retail Industry
According to a survey in 2023, 20% of retail directors in the US and EMEA regions used AI computer vision to automate manual tasks. In retail, AI computer vision is used to identify bottlenecks, customer attention, and waiting times. Video surveillance cameras scrupulously scan customers and track their movements throughout their stay. The system also counts people and performs footfall analysis.
- AI Computer Vision in Business
AI computer vision has significantly streamlined various key operations in businesses. It helps track employees’ productivity, detect improvement gaps, and check attendance. Consequently, it automates workflow, saves costs, and increases work efficiency. According to BusinessDasher, 42% of companies are considering implementing AI computer vision technology.
- AI Computer Vision in the E-commerce Industry
Do you know? According to SellersCommerce, 33% of US companies have fully employed AI computer vision in their key operations. In the e-commerce industry, AI computer vision scans virtual warehouses, monitors virtual salespersons, and searches for products virtually.
- AI Computer Vision in Sports
AI computer vision has also played a huge role in improving sports analytics systems. It helps track players’ movement and ball trajectories during the game. It also provides real-time insights for fans and sports coaches. Moreover, it also helps to provide instant graphical visuals during live broadcasting.
- AI Computer Vision in the Agriculture Industry
AI computer vision significantly helps farmers detect diseases, insects, and crop anomalies in the agriculture industry. It also enables animal monitoring for animal welfare. According to MarketsandMarkets, the AI-powered agriculture industry is projected to reach US$4.7bn by 2028.
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