According to the recent research report "AI in Computer Vision Market by Component (Hardware, Software), Vertical (Automotive, Sports & Entertainment, Consumer, Robotics & Machine Vision, Healthcare, Security & Surveillance, Agriculture), and Region - Global Forecast to 2023", the AI in computer vision market is expected to be valued at USD 3.62 Billion in 2018 and is expected to reach USD 25.32 Billion by 2023, at a CAGR of 47.54% between 2018 and 2023.
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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 2016, 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.
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