The fast adoption of Artificial Intelligence (AI) technologies is mainly responsible for the huge growth in the Computer Vision market. Artificial intelligence (AI) is growing and changing quickly in the Computer Vision field, which is affecting many different businesses. First, the growing need for automation in fields like healthcare, auto industry, and more is driving the use of AI in computer vision. As a result of organizations' need for smart and efficient systems to simplify processes and boost output, the market is growing. Machine learning methods are always getting better, which is another important factor driving the AI in Computer Vision market.
As these algorithms get smarter, they make it possible for computer vision systems to understand and examine visible data more accurately than ever before. AI study and development is always going on, which helps programs change over time. This makes Computer Vision apps stronger and more flexible. The ability to easily reach huge datasets is a key market factor that has a big impact on how well AI works in computer vision. Better accuracy and dependability will come from training machine learning models with more fully labeled datasets as they become available. The Computer Vision market is growing because there is a lot of data and cloud computing is getting better, which makes it easier to learn and use complex AI models.
Also, the use of AI in computer vision is growing because technology parts are getting cheaper and easier to find. Computer Vision systems can handle more data because they have high-performance GPUs (Graphics Processing Units) and hardware built specifically for AI jobs. Hardware prices have gone down, which makes AI easier for more businesses to use. This encourages market growth and new ideas. Another important thing that is changing the AI in Computer Vision market is the rise of edge computing. Edge computing lets you handle data in real time at the source, which cuts down on delay and makes Computer Vision apps work better.
This is especially important when you need to make a decision right away, like in self-driving cars or monitoring systems. Building AI into the edge of computer vision systems makes them smarter and better able to respond to changing surroundings. Lastly, the rising focus on user experience and contact between humans and machines reinforces AI's growing role in Computer Vision. Adding Computer Vision technologies to things like augmented reality, virtual reality, and human-computer connections makes the whole experience better for the user.
Report Attribute/Metric | Details |
---|---|
Market Size Value In 2022 | USD 12,443.9 Billion |
Market Size Value In 2023 | USD 16,916.2 Billion |
Growth Rate | 36.0% (2023-2032) |
The AI in Computer Vision market is projected to grow from USD 23.01 Billion in 2024 to USD 268.36 Billion by 2032, exhibiting a compound annual growth rate (CAGR) of 35.94% during the forecast period (2024-2032). Additionally, AI in Computer Vision Market Size was valued at USD 16,91 Billion in 2023.
The market is substantially growing with advent of technology in application markets such as automotive, consumer electronics, healthcare, agriculture, robotics and more. The use of AI in computer vision is impeccable in terms of market development, ease of manufacturing, market positioning of products, supply efficiency, monitoring of inventory & product movement and more. The demand for AI in computer vision market is anticipated to be driven by rising demand for automation and quality inspection worldwide across end-use markets. Further, government across the globe are promoting businesses by supporting integration and automation of AI in computer vision to create opportunities for market participants in the ecosystem. However, the market reflects security concerns aligned to analytics and cloud-based image processing, which is anticipated to act as a restraining factor to market development over the foreseeable future.
FIGURE 1: AI IN COMPUTER VISION MARKET 2023 - 2032 (USD BILLION)
Source: Secondary Research, Primary Research, MRFR Database, and Analyst Review
Covid-19 pandemic brought in saturation in terms of manufacturing unit and daily work of facilities. The use of AI in manufacturing sector has been undergoing turbulence during the pandemic. Government across the globe has been working on steering technological advancements in the field to cope up with thew rise in pandemic situation and sustain in the marketspace. The healthcare sector however reflected heavy growth in penetration of AI technology due to increased hospitalization and increased dependency of patients on healthcare facilities globally. Rest of the manufacturing sectors, however, reflected moderately lower penetration and application of AI in their operations during the pandemic.
Mobile Edge Computing is an emerging concept which is a paradigm that facilitates mobile devices which are resource-scarce to achieve high capabilities and therefore execute data-intensive applications while collaborating with network servers that are resource rich to enable ubiquitous computing. The use of edge computing in mobile devices are not particularly reliant on certain applications but are predominantly discussed under multiple cases. For instance, edge computing in mobile devices is utilized in connected vehicles in case of vehicles that are autonomous. The functionality is also prominent in case of virtual reality, augmented reality, and enterprise mixed reality applications, cloud gaming applications, real-time detection of drones, video analytics, and more.
The demand for computer vision is spread across an array of application markets wherein few of the non-traditional emerging applications include utilizing of the technology in traffic flow monitoring, road condition monitoring, CT scan & MRI scan in healthcare sector, and more. Increasing demand for computer vision in overcrowded roads is increasing across Asia Pacific region. In bigger cities across the region, computer vision plays a crucial role in diverting traffic and monitoring traffic flow on roads. The system includes few of the key steps including accepting video feedback from camera files and marking of vehicles counting number of vehicles on road in a particular time frame.
Traffic monitoring system includes two different approaches to computer vision technology utilization that includes the following; number-plate recognition system and generic road-traffic monitoring system. In the first system, the computer monitoring system utilizes the available number-plate recognition to identify and monitor vehicles. In the second technology, the computer system runs on identifying vehicles through make & model and monitoring vehicles through complex road scenes. Ease of road traffic monitoring is one of the key emerging application areas that is projected to drive demand for computer vision over the foreseeable future.
Machine vision is coming out to prove its ideal characteristics in shaping human task across industrial sector. With advent of computer vision in every aspect of manufacturing, there has been an ease in process and more profitability. This has led to increased demand in machine learning technology. However, lack of awareness among the end users is one of the challenges faced by industry participants worldwide. Ease of metrology, identification, and execution is a critical portion of the market development. However, industry participants are reluctant in understanding the core concepts of machine vision learning. These factors are poised to reflect substantial challenge in market development over the coming years globally.
The use of AI in computer vision is fragmented into hardware and software. Hardware was further split into processors, network and memory, whereas software was divided into AI platforms and AI solutions. The undivided attention to details for each of the components are highly fragmented based on technological advancements and use of AI across these components, thereby driving market demand and growth over the forecast period. Among components, Software segment expected to witness rapid growth over the forecast period.
FIGURE 2: AI IN COMPUTER VISION MARKET, BY COMPONENT, 2022 VS 2032 (USD BILLION)
Source: Secondary Research, Primary Research, MRFR Database and Analyst Review
The AI in Computer Vision Market, in this report, has been segmented on the basis of machine learning process into Supervised Learning, Unsupervised Learning and Reinforcement Learning. Reinforcement Learning segment will be the fastest growing segment over the forecast period.
Machine learning, in a broader sense, in computer vision is a breakthrough that is increasingly in demand for startup founders, engineers, computer scientists and more. The implication of machine learning in computer vision is to perform tasks without instructions through AI. Machine learning is considered as an integral component of AI wherein computer vision is a direct subset of Artificial Intelligence.
Reinforcement learning is yet another key areas of machine learning which enables learning through process of trial and error, wherein the system utilizes its own feedback from experiences and actions to implement changes. The basic elements of reinforced learning model revolves around analysis through environment, current situation, reward, policy, and value. Few of the highly utilized reinforcement machine learning algorithms include Q-learning and State-Action-Reward-State-Action, and Deep Deterministic Policy Gradient. In simpler terms, reinforcement machine learning is about making sequential decisions wherein the current input decides the output and the subsequent input is based on the obtained output.
The AI in Computer Vision Market is segmented, based on component, into home security, home automation, home entertainment, home healthcare, and others healthcare segment will be the fastest growing segment over the forecast period.
Geographically, the AI in Computer Vision market has been categorized into North America, Europe, the Asia-Pacific, the Middle East & Africa, and South America.
North America is likely to be the dominant regional market. North America is considered a major hub for AI globally, with US capturing the maximum market share of 89.54% in North America in 2020. The country is hub to major AI reforming companies that have been increasingly investing in innovations and catering majority of end use markets across US and North America. Few of the leading AI suppliers in US as of 2021 includes AIBrain, AEye, Anki, AlphaSense, CaseText, Blue River Technology, ClarifAI, CognitiveScale, DataRobots, and CloudMinds, Abnormal Security, ASMP Robotics, Arize AI, Atomwise, Bearing, Canvas, and Cresta among others.
FIGURE 3: AI IN COMPUTER VISION MARKET SHARE BY REGION 2022 VS 2032 (USD BILLION)
Source: Secondary Research, Primary Research, MRFR Database and Analyst Review
The AI in Computer Vision Market is distinguished by the presence of numerous global, regional, and local players catering to the growing demand for edge computing in mobile devices, growing impact of ai in machine vision and increasing demand for computer vision systems in non-traditional and emerging applications. However, high cost of implementation and lack of awareness and technical expertise may hamper the growth of the AI in Computer Vision Market. The major players have adopted a strategy of obtaining regulatory approval from government agencies for their products and signing contracts and agreements to broaden their reach and reduce operational costs. For instance, in November 2020, FLIR Systems, Inc. launched the FLIR VS290-32, an industry-first videoscope that combines thermal imaging and a visible camera specifically designed for safer and more efficient inspections of hard-to-reach underground utility vaults. The VS290-32 is the company's first industrial-grade, electrical safety-rated, flexible dual-sensor videoscope on a replaceable, two-meter-long camera probe. In August 2020, Omron Automation Americas launched a complete machine vision solutions package that can be easily installed on PC-based systems. The new FJ2 cameras feature state-of-the-art complementary metal-oxide-semiconductor (CMOS) sensors, frame rates as fast as 282 frames per second (FPS), and resolutions ranging from 0.4MP up to 5MP in both monochrome and color versions.
January 2022:Â Meta and Penguin Computing, Inc., a division of SGH and a leader in high-performance computing (HPC) focused on artificial intelligence and machine learning, partnered for providing AI-optimized architecture and managed services for the AI Research SuperCluster (RSC).
November 2021:Â Microsoft's low code application development advanced specialization was earned by UST, a California-based digital transformation solutions company.
The key vendors in the market are Ultraleap, Irida Labs S.A., Microsoft Corporation, Clarifai, Inc., General Electric, Xilinx, Inc., Omron Corporation, Nvidia Corporation, Qualcomm Technologies Inc., Meta Platforms Inc. Google, LLC, Apple Inc, Intel Corporation, Teledyne Technologies Inc, and Prisma AI
January 2020:Â Qualcomm announced the expansion of its Qualcomm Snapdragon Ride Platform portfolio with safety-grade system-on-chips (SoCs) designed for automotive safety integrity level D (ASIL-D) systems. This provides the flexibility of using a single SoC for New Car Assessment Program (NCAP) Level 1 advanced driving assistance systems (ADAS) and Level 2 automation systems.
April 2021:Â Microsoft Corporation acquired Nuance Communications, Inc. Nuance is a pioneer and a leading provider of conversational AI and cloud-based ambient clinical intelligence for healthcare providers.
October 2021: Clarifai launched Clarifai Community–the World's AI Community, built for AI creators and developers. The community enables users to develop and share AI resources throughout their enterprise or organization, even collaborating with the public. These capabilities can propel organizations to deploy AI across various use cases and applications.
North America
Europe
Asia-Pacific
Middle East & Africa
South America
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