The AI in Computer Vision Market was valued at USD 2.37 billion in 2020 and is expected to reach USD 25.32 billion by 2027, at a CAGR of 47.54% during the forecast period.
The base year for calculation in this AI in Computer Vision Market business report is taken as 2020 while the historic year is 2019 which will tell how the market will perform in the forecast years by informing what the market definition, classifications, applications, & engagements are. This AI in Computer Vision Market report is also all-embracing of the data which covers market definition, classifications, applications, engagements, market drivers & market restraints that are based on the SWOT analysis. A large scale AI in Computer Vision Market report is a comprehensive study about the market which tells about the market status in the forecast period of 2021-2027.
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The AI in computer vision market was valued at USD 2.37 billion in 2020 and is expected to reach USD 25.32 billion by 2027, at a CAGR of 47.54% during the forecast period. In this report, 2020 has been considered as the base year, and the forecast period is from 2021 to 2027. The objective of the report is to define, describe, and forecast the AI in computer vision market, in terms of value, on the basis of vertical, component, and geography. The major factors that are expected to be driving the AI in Computer Vison Market include growing demand for edge computing in mobile devices, increasing demand for computer vision systems in non–traditional and emerging applications and growing impact of AI in machine vision.
Consumer vertical expected to hold the largest market size during the forecast period
The AI in computer vision market, by vertical, is expected to be valued at USD 2.37 billion in 2020 and is likely to reach USD 25.32 billion by 2027, at a CAGR of 47.54% during the forecast period. The market for the automotive is expected to be valued at USD 0.17 billion in 2020 and is likely to reach USD 2.66 billion by 2027, at the highest CAGR of 56.30% during the forecast period. This market growth can be attributed to the initiatives taken by the Chinese government to support AI by providing funding for research and development, support for workforce development, and suggestions for international collaboration and expansion. Also, various Chinese players are collaborating with leading players to train deep neural networks for machine learning and Al. For instance, in September 2020, NVIDIA Corporation (US) introduced a new technology to help drive revenue in the China’s AI market for machine learning. Also, Google Inc. (US) and Apple Inc. (US) launched their AI-based smartphones—Google Pixel 2/Pixel 2X, iPhone 8 plus, and iPhone X—that have various features such as user authentication, emotion recognition, personal profiling, and others, which would improve usability and self-service capabilities.
Market for GPU in APAC is expected to grow at the highest CAGR during the forecast period
The AI in computer vision market, for GPUs in APAC is expected to be valued at USD 159.4 million by 2027, growing at a CAGR of 71.68% during the forecast period. The market for GPUs in APAC is expected to grow at the highest CAGR during the forecast period because of potential customer base in the region. Additionally, with decreasing average selling price (ASP), there is an increasing demand for laptops. The increasing demand for advanced GPUs in the gaming segment drives the growth of the AI in computer vision market in APAC.
Market for AR/VR in APAC is expected to grow at the highest CAGR during the forecast period
The AI in computer vision market for VR/AR in APAC was valued at USD 1.1 million in 2020 and is likely to reach USD 55.8 million by 2026, at a CAGR of 91.14% during the forecast period. The AR/VR players in APAC are increasingly adopting AI based processors in AR/VR headsets. For instance, in June 2019, Movidius (US) signed a partnership with Lenovo (China) to provide advanced vision processing technology for a variety of VR-centric Lenovo products. According to this partnership, Lenovo will be able to source the advanced Myriad 2 vision processing unit (VPU) and custom computer vision algorithms for various virtual reality projects. Also, Apple Inc. (US), Facebook, Inc. (US), and Google Inc. (US) are focused on deploying VR and AR in smartphones that require graphics cards to operate.
APAC expected to grow at the highest CAGR during the forecast period
The AI in computer vision market in APAC is expected to be valued at USD 0.60 billion in 2020 and is likely to reach USD 10.06 billion by 2027, at the highest CAGR of 59.14% between 2021 and 2027. The growth in India can be attributed to the growing investment across sectors in building data-based businesses and the recognition by the Government of India to create enabling policies for AI. Also, the government is providing a funding of approximately USD 477 million to promote machine learning, 3D printing, and other technologies in the country.
Driver: Growing demand for edge computing in mobile devices
Most AI algorithms need a large amount of data and computing power to accomplish tasks. For this reason, they rely on cloud servers to perform their computations; they are not capable of accomplishing tasks on devices, mobile phones, computers, and other devices. This limitation makes AI algorithms inefficient in settings where connectivity is sparse and where operations need to be performed in time-critical situations. Premium smartphone vendors are exploring SoC design and frameworks that will bring AI closer to the edge. The connectivity in mobile devices suffers from latency, network congestion, signal collision, and huge geographic coverage. These are few challenges that we face when processing edge data in the cloud. The dedicated chipset in mobile devices can help compute resources in real time and execute algorithms without the need for a round-trip to the cloud.
Apart from smartphones, drones, augmented reality, and driverless cars need to run real-time deep learning. Any delay because of the communication with the cloud can result in disastrous or fatal events. Also, in case of a network disruption, a total halt of operations is imaginable. At this stage of the market, Apple (US) and Google (US) are using AI processors in their flagship smartphones products. However, with the growth of AI in autonomous cars, drones, and other mobile devices, other players are also expected to enter in this market space.
Restraint: Lack of awareness and technical knowledge
The increasing competition in the manufacturing sector and the growing demand of customers for better products at a competitive price have made advanced automation essential for industrial and non-industrial applications. However, various industries such as sports and entertainment, robotics and machine vision, and healthcare are not convincingly adopting AI-enabled computer vision systems due to the lack of awareness and technical expertise. There is a need to make end-customers understand about the benefits of the 3D or 4D computer vision technology. For instance, stereo vision technology uses additional data of height/depth of every point in the image. This technology can precisely measure the distance to an object by comparing two images and detect defects that are difficult to identify with 2D computer vision systems. It can inspect the shape of bottles and bent pins with a connector. Thus, 3D computer vision is helpful in the environment wherein the size of the objects is not fixed.
As upgrading 3D and 4D computer vision technologies are becoming complex and sophisticated day by day, there is an increasing need for regular training courses and workshops for customers. The training takes time, and poor programming can produce inaccurate results. This would contribute to the higher operating cost of computer vision systems.
Opportunity: Development of machine learning regarding vision technology
Applications that require speed, high resolution, and good sensitivity to light are expected to push the vision system technology forward. The future development of technologies, individual components, or complete systems is expected to concentrate mainly on enhanced resolution and speed, greater sensitivity, easier integration capabilities, and faster interfaces. These innovations also ensure constant advancements in new industries and for non-industrial applications. Advancement in camera dynamic range and resolution, computational cameras, real-time detection of moving objects, use of color information, analysis of point clouds, and cloud computing of machine vision are some of the technological developments that the computer vision industry is going to experience in the future.
For instance, Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models. It classifies images into thousands of categories, detects individual objects and faces within images, and finds and reads printed words contained within images. It builds metadata on the user’s image catalog, or enable new marketing scenarios through image sentiment analysis. Furthermore, it analyzes images uploaded through a request or integrates with your image stored in Google Cloud.
In August 2019, Ford (US) acquired SAIPS (Israel) to integrate human-like intelligence into machine learning components of driverless car systems. SAIPS technology focuses on image- and video-processing algorithms and deep learning that enables processing and classifying input signals. The SAIPS technology enables on-board interpretation of data captured by sensors in Ford’s self-driving cars, and turns that data into usable information for the car’s virtual driver system.
This has enabled the development of machine learning into autonomous vehicles using vision technology.
Challenge: Premium pricing of AI hardware
AI is rapidly being incorporated into diverse applications in the cloud and at the network’s edge. AI hardware needs to be miniaturized into low-cost, reliable, high-performance chips to increase the adoption of AI. Companies such as Google (US), Apple (US), and Huawei (China) are including AI hardware components and software solutions in their flagship smartphones. These smartphones use AI in all applications ranging from imaging and photography to power efficiency and security. According to the recent trend, it can be predicted that the AI chipset market will grow rapidly by 2022; the price will drop approximately below USD 25 per chip, and the open-source ecosystem for real-time Linux on DL SOC will emerge. This as a result will trigger mass adoption of AI edge-client chipsets for mobiles and PCs/laptops, e.g., Snapdragon, an application processor that gives mobile phones the computing power to run sophisticated applications and software. Snapdragon is also used by other companies such as Toshiba (Japan), Acer (Taiwan), and Google (US). The short-term impact of premium cost of edge-based processors is expected to be a major challenge. However, in the long term, the overall economy of scale of the manufacturers of AI processors is expected to minimize the overall impact during the forecast period.
AI in Computer Vision Market, by Component:
AI in Computer Vision Market, by Vertical:
- Sports and Entertainment
- Robotics and Machine Vision
- Security and Surveillance
AI in Computer Vision Market, by Region:
- North America
Key Market Players
NVIDIA (US), Intel (US), Apple (US), Alphabet (US), Qualcomm (US), Microsoft (US), Basler (Germany), Teledyne (US), Wikitude (Austria), Cognex (US), National Instruments (US), ISRA Vision (Germany), Avigilon (Canada), and General Electric (US)
NVIDIA is among the world leaders in the visual computing business. It has a well-established geographic footprint and deals with major OEMs or ODMs. The company continues to lead in the development of new products for the AI in computer vision market. Recently, NVIDIA has witnessed significant jump in its data center revenue. It is giving tough competition to Intel in the data center market. NVIDIA DRIVE for automakers to build and deploy self-driving cars and trucks that are functionally safe and certified to international safety standards is one of the important products of the company. NVIDIA is well ahead in the R&D of autonomous cars when compared to its competitors.
- In January 2021, NVIDIA announced a new architecture of NVIDIA DRIVE, its AI autonomous vehicle platform that uses redundant and diverse functions to enable vehicles to operate safely. The architecture enables automakers to build and deploy self-driving cars and trucks that are functionally safe and certified to international safety standards, e.g., ISO 26262.
- In September 2020, Intel launched Computer Vision SDK that allows developers to make their computer vision applications more accurate and faster. Computer Vision SDK combines video processing, computer vision, machine learning, and pipeline optimization into a single package.
- In February 2021, Google (US) launched tensor processing units (TPUs) to run specific machine learning workflows significantly faster than the standard GPUs.
- In February 2021, Qualcomm (US) introduced its Qualcomm Artificial Intelligence (AI) Engine, which comprises several hardware components and software solutions that accelerate on-device AI-enabled user experiences on Qualcomm Snapdragon mobile platforms.
- In September 2020, Apple acquired Regaind (France) to access Regaind’s computer vision application programming interface (API) and analyze the content of photos.
All viewpoints in the report are based on iterative validation by engaging influencer, experts of the market, whose opinions supersede all other research methodologies. Both primary and secondary approach are used and detailed product portfolio / service offering were analysed and have been presented in a separate chapter of competitive landscape along with company profile.
Detailed Insights on Market Concentration Rate:
CR4, CR8 and HHI Index Analysis of Overall Market
Comparative Market Share Analysis (Y-o-Y)
Emerging Players – Heat Map Analysis
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Table of Content:
Chapter 1: Introduction and Scope
Chapter 2: Key Company Profiles
Chapter 3: AI in Computer Vision Market Explanations, Share and Forecast across type, application and geography
Chapter 4: AI in Computer Vision Industry Consumption by Regions
Chapter 5: Market Explanation of Asia Pacific region
Chapter 6: Market Explanations of Europe region
Chapter 7: Market Explanation of Asia Pacific region
Chapter 8: Market Explanations of North America region
Chapter 9: Market Explanations of Middle East and Africa region
Chapter 10: Key Important features of the AI in Computer Vision market
Chapter 11: Key trends of the market and the AI in Computer Vision market Opportunities
Chapter 12: Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 13: Research Findings and Conclusion
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