The AI in Computer Vision Market is expected to expand significantly, from its estimated value of USD 17.2 billion in 2023 to USD 45.7 billion by 2028. Between 2023 and 2028, this translates into a compound annual growth rate (CAGR) of 21.5%.

The hardware and software aspects of AI in computer vision are the main topics of the market study. Additionally, it emphasises various computer vision operations like inference and training. While both industrial and non-industrial sectors can apply AI in computer vision, the end-use industries are predominantly the automobile and consumer electronics. Advanced algorithms and machine learning methods are used in computer vision AI to provide computers the ability to comprehend, interpret, and analyse visual data. Due to its capacity to better decision-making, automate processes, and increase productivity across a range of industries, this technology has experienced substantial growth. AI in computer vision helps the automobile sector with programmes like object identification, driver assistance systems, and autonomous vehicles. The consumer electronics industry, on the other hand, uses computer vision for applications including picture identification, augmented reality, and facial recognition.

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NVIDIA Corporation (US), Intel Corporation (US), Microsoft (US), IBM Corporation (US), Qualcomm Technologies Inc. (US), Advanced Micro Devices, Inc (US), Alphabet, Inc. (US), Amazon (US), Basler AG (Germany), Hailo (US), and Groq, Inc. (US). The other company profiles included in the scope are Sighthound, Inc. (US), Neurala, Inc. (US), Datagen (Israel), Graphcore (UK), Groopic (US), Ultraleap (US), Algolux (US), Athena Security (US), Snorkel AI (US), Vizseek (US), Robotic Vision Technologies (US), AMP Robotics (US), CureMetrix (US), and Creative Virtual (UK) are among the many players in the AI in computer vision market.

Numerous trends and upheavals in the field of AI computer vision are having a substantial impact on client enterprises. The development of deep learning methods is one of the main forces behind advancements in AI computer vision. The capabilities of AI computer vision systems have been revolutionised by deep learning, a kind of machine learning. AI computer vision systems are improving their knowledge and effectiveness in interpreting visual data by utilising deep learning algorithms and massive datasets.

The rising use of edge computing is another trend reshaping the AI computer vision scene. Data is processed closer to its source using the distributed computing paradigm known as edge computing rather than being sent to a central point for analysis. Due to its ability to enable real-time processing, lower latency, and increase the effectiveness of visual data analysis, this approach is becoming more and more popular in AI computer vision. AI computer vision systems can make quicker, more accurate decisions by processing data at the edge, which improves results and improves user experiences.

Increased market competition is another effect of AI computer vision's rising popularity. Businesses are aiming to distinguish out from the competition by offering distinctive and new solutions. The rush to provide cutting-edge AI computer vision technology has sparked quick developments and a never-ending quest for perfection in the industry. Customers gain from this competition because they have access to a variety of options and cutting-edge solutions to meet their unique needs.

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Increasing use of AI computer vision in autonomous systems

New and creative applications are being driven by the growing usage of AI computer vision in autonomous systems across numerous industries. AI computer vision technology is used by autonomous systems, such as self-driving cars, drones, and robots, to observe and comprehend their surroundings. These systems can use this capability to base their decisions and actions on the visual input they receive. The advancement of AI computer vision is essential to the creation of self-driving cars. The information gathered by the cameras and sensors mounted in the car can be analysed in real-time by utilising AI algorithms. The system can identify and recognise objects like pedestrians, cars, road signs, and traffic lights thanks to this processing. With this knowledge, the self-driving car can make wise decisions about how to move around its surroundings safely and effectively without human assistance. Beyond self-driving cars, AI computer vision is used in autonomous systems. For instance, drones may use computer vision technology to recognise and track items, keep an eye on landscapes, and help with a variety of activities like delivery, inspection, and surveillance. Robots with AI computer vision capabilities are able to observe and comprehend their surroundings, which enables them to carry out difficult jobs in unpredictable circumstances. There are several opportunities for companies as a result of the growing use of AI computer vision in autonomous systems. In industries like transportation, logistics, manufacturing, agriculture, and healthcare, it improves safety, efficacy, and productivity. AI computer vision technology-equipped autonomous systems have the potential to revolutionise industries and lead to major improvements in automation and efficiency.

Emerging applications of AI computer vision in agriculture, logistics, and manufacturing

Agriculture, shipping, and manufacturing are just a few of the many industries where AI computer vision is quickly finding new applications. These businesses stand to gain significantly from these applications. Automation of different processes in agriculture is revolutionising traditional farming methods. It makes it possible for farmers to more effectively monitor crops, forecast yields, and find disease outbreaks. AI systems can gain important insights into the growth, health, and potential production of crops by examining photographs of them. By enabling farmers to make knowledgeable decisions about irrigation, fertilisation, and pest management, this information maximises crop output and raises total agricultural productivity. Another industry that gains from the use of AI computer vision is logistics. Object detection, tracking, and sorting are just a few of the automated processes that computer vision algorithms may do on photos and videos. This technology makes warehouse operations more efficient, improves the accuracy of package handling, and streamlines inventory management. Logistics firms may increase operational effectiveness, lower errors, and boost customer happiness by automating these procedures. AI computer vision is revolutionising manufacturing industry production processes. Computer vision algorithms can keep an eye on production lines, spot flaws instantly, and guarantee product quality. The efficiency, waste reduction, and quality control of manufacturing processes can all be improved by incorporating AI computer vision systems. Through the automation of visual inspections, the technology enables quicker and more precise fault detection, increasing manufacturing productivity and reducing costs.

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GPU is expected to hold the highest CAGR for the hardware segment during the forecast period.

Within the hardware segment, the graphic processing unit (GPU) is anticipated to experience the highest compound annual growth rate (CAGR) during the projection period. In computing systems, GPUs are often used, especially in the consumer market. GPUs do better than central processing units (CPUs) at efficiently handling large, complicated data sets. In particular, 3D applications like computer games and 3D authoring tools are preferred. Key participants in the CPU industry include NVIDIA, Qualcomm, and Intel, with NVIDIA standing out in particular. NVIDIA GPUs are used in industrial inspection systems for the real-time processing and analysis of picture data obtained from cameras and other sensors. NVIDIA's Jetson platform is used by a lot of businesses in the industrial inspection space to carry out AI vision tasks like object identification, image segmentation, and classification. The 256-core NVIDIA Pascal GPU architecture used by NVIDIA's Jetson platform includes 256 NVIDIA CUDA cores for improved performance and scalability. By effectively processing and analysing visual input, our high-performance GPU architecture satisfies the stringent requirements of industrial inspection systems.

AI in the computer vision market in North America to hold the highest market share during the forecast period

During the projected period, North America is anticipated to hold the largest market share in the computer vision market for AI. The region's dominance in this sector can be linked to a number of things, including the funding and support that US entrepreneurs have gotten for their efforts in AI research. With the help of these funds, they are able to investigate how AI technology might be used in self-driving drones and other aerial vehicles. Addressing issues with industrial drone reliability, safety, and autonomy is the main area of focus in the US. Companies have created cutting-edge solutions that integrate computer vision and deep learning techniques to address these issues. These technologies can help drones recognise possible dangers in their environment and increase their endurance, leading to safer and more effective operations. Furthermore, the advancement of computer vision is greatly influenced by colleges and research facilities in the US. They actively contribute to the advancement of the subject through research and innovation and have powerful computer vision programmes. These organisations support the exchange of ideas between academia and business, fostering the development and uptake of AI in computer vision.

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https://www.globenewswire.com/en/news-release/2023/02/24/2615196/0/en/AI-and-Computer-Vision-A-Powerful-Combination-for-Growth-in-the-Coming-Years.html

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