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1401307

全球人工智慧影像辨识市场 - 2023-2030

Global AI Image Recognition Market - 2023-2030

出版日期: 2023年12月29日 | 出版商:

DataM Intelligence | 英文 206 Pages | 商品交期: 约2个工作天内

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简介目录

概述

全球人工智慧影像辨识市场在2022年达到19亿美元，预计到2030年将达到46亿美元，2023-2030年预测期间CAGR为11.8%。

全球所有行业的自动化趋势推动了人工智慧影像识别在品质控制、检查和监控等任务中的应用，这有助于推动人工智慧影像辨识市场的成长。自动化提高了营运效率并减少了对重复性视觉任务的人为干预。人工智慧影像辨识在医疗保健、零售、汽车、安全和农业等多个行业都有应用。影像辨识技术的多功能性有助于其在解决行业特定挑战方面广泛采用。

在电子商务和零售业，人工智慧图像识别为视觉搜寻、产品推荐、库存管理和客户参与提供了机会。增强的使用者体验和个人化服务推动了零售业的采用。智慧城市措施为人工智慧影像辨识在城市规划、交通管理、公共安全和环境监测方面提供了机会。影像辨识技术的整合有助于高效和永续城市的发展。

AI视觉辨识在北美医疗保健产业的应用日益广泛，大大助力了AI影像辨识技术的市场拓展。例如，2022 年，美国将 GDP 的近 26% 用于医疗保健设施。在北美，消费者将部分额外现金花在医疗保健上。该地区医疗保健行业的快速成长有助于推动人工智慧图像识别市场的成长。

动力学

不断成长的人工智慧采用率

随着全球对人工智慧技术的认识和理解不断增强，各行业的企业都认识到将人工智慧图像识别整合到其营运中的潜在好处。这种理解有助于探索和投资人工智慧解决方案。人工智慧（尤其是深度学习和神经网路）的不断进步带来了更复杂和准确的影像辨识模型。技术改进提高了人工智慧影像辨识系统的整体有效性和可靠性，推动了其采用。

根据《哈沃德商业评论》2021 年提供的资料，约 52% 的公司因新冠危机而加速了人工智慧采用计画。 86% 的参与者表示，人工智慧将在2021 年成为其公司的「主流技术」。哈里斯民调与Appen 合作发现，55% 的公司表示，由于COVID-19，他们在2020 年加快了人工智慧策略，67% 的公司表示，他们在2020 年加快了人工智慧策略。预计在 2021 年进一步加速他们的人工智慧策略。72% 的调查参与者对人工智慧在未来发挥的作用持积极态度。

技术进步

深度学习架构的进步显着提高了人工智慧影像辨识模型的准确性和效能。增强的演算法和模型架构有助于获得更精确的影像辨识结果。深度学习的技术进步，特别是更深层神经网路的发展，使人工智慧系统能够学习影像中复杂的模式和特征。这导致了影像辨识任务的突破，包括目标侦测和分类。

技术进步使人工智慧影像辨识系统能够即时处理和分析视觉资料。这对于监控、自动驾驶汽车和即时视讯分析等需要即时决策的应用至关重要。例如，2020 年 3 月 8 日，奥地利光子学研究所的研究人员利用神经网路的光感电子元件在小晶片上创造了人造眼。据报道，在维也纳提出的新设计可以在几纳秒内识别物体，这是该技术的重大进步。

AI影像辨识缺乏标准化

如果没有标准化的实践，不同人工智慧影像辨识系统之间的互通性就变得具有挑战性。缺乏相容性阻碍了影像辨识解决方案与现有工作流程和技术的无缝整合。缺乏标准化基准和评估指标导致不同影像辨识模型的表现不一致。这使得企业和用户很难比较并选择最适合其特定需求的解决方案。

标准化对于公平、准确地评估不同的人工智慧影像辨识模型至关重要。缺乏标准化的评估标准使得开发者和使用者难以客观地比较各种模型的表现。标准化在确保人工智慧系统的透明度和可解释性方面发挥关键作用。如果没有标准化的模型解释和解释方法，使用者很难理解人工智慧图像识别系统如何做出具体决策，从而导致信任问题。

The application of AI image recognition for tasks like quality control, inspection and monitoring is fueled by the global movement toward automation in all industries which helps to boost the market growth of the AI image recognition market. Automation enhances operational efficiency and reduces human intervention in repetitive visual tasks. AI image recognition finds applications in diverse industries, including healthcare, retail, automotive, security and agriculture. The versatility of image recognition technologies contributes to their widespread adoption in solving industry-specific challenges.

In the e-commerce and retail industry, AI image recognition presents opportunities for visual search, product recommendation, inventory management and customer engagement. Enhanced user experiences and personalized services drive adoption in the retail sector. Smart city initiatives provide opportunities for AI image recognition in urban planning, traffic management, public safety and environmental monitoring. The integration of image recognition technologies contributes to the development of efficient and sustainable cities.

The growing application of AI visual recognition in North America's healthcare industry has greatly assisted the market expansion of AI image recognition technology. For instance, in 2022, U.S. spent almost 26% of its GDP on healthcare facilities. In North America, consumers spend a portion of their extra cash on healthcare. The rapid growth of the healthcare industry in the region helps to boost the market growth of AI image recognition.

Dynamics

Growing AI Adoption

As awareness and understanding of AI technologies have grown globally, businesses across various industries recognize the potential benefits of integrating AI image recognition into their operations. The understanding has contributed to a willingness to explore and invest in AI solutions. Ongoing advancements in AI, particularly in deep learning and neural networks, have resulted in more sophisticated and accurate image recognition models. The technological improvements have increased the overall effectiveness and reliability of AI image recognition systems, driving adoption.

According to the data given by Harward Business review in 2021 about 52% of companies accelerated their AI adoption plans because of the COVID crisis. 86%, of participants say that AI is becoming a "mainstream technology" at their company in 2021. Harris Poll, working with Appen, found that 55% of companies reported they accelerated their AI strategy in 2020 due to COVID-19 and 67% expect to further accelerate their AI strategy in 2021. 72% of participants in the survey feel positive about the role that AI play in the future.

Technological Advancements

Advances in deep learning architectures have significantly improved the accuracy and performance of AI image recognition models. Enhanced algorithms and model architectures contribute to more precise image recognition results. Technological advancements in deep learning, especially the development of deeper neural networks, enable AI systems to learn intricate patterns and features within images. The has led to breakthroughs in image recognition tasks, including object detection and classification.

Technological advancements have enabled AI image recognition systems to process and analyze visual data in real time. The is critical for applications such as surveillance, autonomous vehicles and live video analytics where instant decision-making is essential. For instance, on March 08, 2020, researchers from the Institute of Photonics, Austria created an artificial eye on a small chip by using light-sensing electronics with a neural network. The new design, presented in Vienna has been reported to identify an object within a few nanoseconds, which is a serious advancement in the technology.

Lack of Standardization in the AI Image Recognition

Without standardized practices, interoperability between different AI image recognition systems becomes challenging. The lack of compatibility hinders the seamless integration of image recognition solutions into existing workflows and technologies. The absence of standardized benchmarks and evaluation metrics lead to inconsistent performance across different image recognition models. The makes it difficult for businesses and users to compare and choose the most suitable solution for their specific needs.

Standardization is crucial for the fair and accurate evaluation of different AI image recognition models. The lack of standardized evaluation criteria makes it challenging for developers and users to objectively compare the performance of various models. Standardization plays a key role in ensuring transparency and explainability in AI systems. Without standardized methods for model interpretation and explanation, users find it difficult to understand how AI image recognition systems arrive at specific decisions, leading to trust issues.

Segment Analysis

The global AI image recognition market is segmented based on component, application, end-user and region.

Growing Adoption of AI Image Recognition Software in Various Industries

Based on the components, the AI image recognition market is segmented into hardware, software and services. AI image recognition software segment is growing over the forecast period 2023-2030. Artificial intelligence recognizes image software is growing in direct proportion to advances in deep learning and neural networks. Convolutional neural networks (CNNs), in particular, are deep learning models that have shown impressive capabilities in image identification obligations, increasing performance and accuracy.

The availability of large and diverse labeled datasets has played a crucial role in training and fine-tuning sophisticated AI image recognition models. Access to extensive datasets allows software developers to create more accurate and generalized image recognition solutions. The availability of open-source deep learning frameworks such as TensorFlow and PyTorch has democratized AI development.

The has empowered developers to create and customize image recognition models, fostering innovation and accelerating the adoption of AI image recognition software. To fulfill consumer's demand for the AI Image Recognition software major key players in the market launched new products. For instance, on April 07, 2023, Meta launched AI tool that identify, separate items in pictures.

Geographical Penetration

Growing Adoption of AI Image Recognition in Various industries of North America

North America accounted for the largest market share in the global AI image recognition market due to the region's well-established and robust IT infrastructure providing a solid foundation for the deployment and integration of AI image recognition systems across various industries. North America is anticipated to have the greatest market size in the global image recognition market.

North America is home to the headquarters of many of the top technology companies in the world, such as Google, Microsoft and IBM. The companies promote AI innovation and adopt the application of cutting-edge image recognition technologies. Major key players in the region launched new services for AI image recognition that help boost the regional market growth of the global AI image recognition market.

For instance, on November 03, 2023 oracle announced Oracle Cloud Infrastructure (OCI) AI services that make it easier for developers to apply AI services to their applications without requiring data science expertise. It gives developers the choice of leveraging out-of-the-box models that have been trained on business-oriented data or custom training the services based on their organization's data.

Competitive Landscape

The major global players in the market include IBM Corporation, Imagga Technologies Ltd, Amazon Web Services, Inc, Qualcomm, Google LLC, Microsoft Corporation, Trax Technology Solutions Pte Ltd, NEC Corporation, Ricoh Company, Ltd and Catchoom Technologies S.L.

COVID-19 Impact Analysis

Geopolitical events result in changes to regulations and policies that govern the development, export or use of certain technologies. Regulatory shifts impact the global market landscape for AI image recognition. During geopolitical conflicts, there is often an increased risk of cyber threats and attacks. As AI image recognition systems deal with sensitive visual data, companies invest more in cybersecurity measures to protect these technologies. Geopolitical instability influence research and development activities in the AI field. Collaboration and knowledge exchange between researchers and institutions in different regions were affected.

Russia-Ukraine War Impact Analysis

Geopolitical tensions and conflicts lead to disruptions in the global supply chain, affecting the production and availability of hardware components and technologies necessary for AI image recognition systems. Heightened geopolitical tensions contribute to overall market uncertainty. Businesses become more cautious about investments, potentially impacting the demand for AI technologies, including image recognition solutions. Geopolitical events result in changes to regulations and policies that govern the development, export or use of certain technologies. Regulatory shifts impact the global market landscape for AI image recognition.

During geopolitical conflicts, there is often an increased risk of cyber threats and attacks. As AI image recognition systems deal with sensitive visual data, companies invest more in cybersecurity measures to protect these technologies. Geopolitical instability influences research and development activities in the AI field. Collaboration and knowledge exchange between researchers and institutions in different regions were affected.

By Component

Hardware
Software
Service

By Application

Augmented Reality
Scanning & Imaging
Security & Surveillance
Marketing & Advertising
Image Search

By End-User

Education
Gaming
Healthcare
Government
Aerospace & Defense
Media & Entertainment
Retail
Banking Financial Services and Insurance
Others

By Region

North America
- U.S.
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Spain
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific
Middle East and Africa

Key Developments

On October 11, 2023, Klarna, a Swedish fintech company launched AI-powered image Recognition Tool in the market. The tool Shopping Lens uses AI to translate images of products into a search term and directs customers to the best deals on Klarna's app.
On December 12, 2023, Mastercard, launched Shopping Muse, an AI tool that gives product recommendations. By using this tool shoppers search for products using search terms such as cottage core or beach formal and receive product recommendations based on their demographic profile, intent, purchase history and other traits.
On October 02, 2023, GoSpotCheck by FORM, the top AI-powered retail execution platform for Beverage Alcohol and CPG launched the latest innovation to its image recognition technology which provides support to the on-premise displays, including beer menu, beer taps, wine menu, cocktail menu and back bar.

Why Purchase the Report?

To visualize the global AI image recognition market segmentation based on component, application, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of AI image recognition market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global AI image recognition market report would provide approximately 61 tables, 65 figures and 206 Pages.

Target Audience 2023

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Component
3.2. Snippet by Application
3.3. Snippet by End-User
3.4. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Growing AI Adoption
  - 4.1.1.2. Technological Advancements
- 4.1.2. Restraints
  - 4.1.2.1. Lack of Standardization
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Component

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 7.1.2. Market Attractiveness Index, By Component
7.2. Hardware*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Software
7.4. Service

8. By Application

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 8.1.2. Market Attractiveness Index, By Application
8.2. Augmented Reality*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Scanning & Imaging
8.4. Security & Surveillance
8.5. Marketing & Advertising
8.6. Image Search

9. By End-User

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 9.1.2. Market Attractiveness Index, By End-User
9.2. Education*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Gaming
9.4. Healthcare
9.5. Government
9.6. Aerospace & Defense
9.7. Media & Entertainment
9.8. Retail
9.9. Banking Financial Services and Insurance
9.10. Others

10. By Region

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 10.1.2. Market Attractiveness Index, By Region
10.2. North America
- 10.2.1. Introduction
- 10.2.2. Key Region-Specific Dynamics
- 10.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.2.6.1. U.S.
  - 10.2.6.2. Canada
  - 10.2.6.3. Mexico
10.3. Europe
- 10.3.1. Introduction
- 10.3.2. Key Region-Specific Dynamics
- 10.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.3.6.1. Germany
  - 10.3.6.2. UK
  - 10.3.6.3. France
  - 10.3.6.4. Italy
  - 10.3.6.5. Spain
  - 10.3.6.6. Rest of Europe
10.4. South America
- 10.4.1. Introduction
- 10.4.2. Key Region-Specific Dynamics
- 10.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.4.6.1. Brazil
  - 10.4.6.2. Argentina
  - 10.4.6.3. Rest of South America
10.5. Asia-Pacific
- 10.5.1. Introduction
- 10.5.2. Key Region-Specific Dynamics
- 10.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.5.6.1. China
  - 10.5.6.2. India
  - 10.5.6.3. Japan
  - 10.5.6.4. Australia
  - 10.5.6.5. Rest of Asia-Pacific
10.6. Middle East and Africa
- 10.6.1. Introduction
- 10.6.2. Key Region-Specific Dynamics
- 10.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

11. Competitive Landscape

11.1. Competitive Scenario
11.2. Market Positioning/Share Analysis
11.3. Mergers and Acquisitions Analysis

12. Company Profiles

12.1. IBM Corporation*
- 12.1.1. Company Overview
- 12.1.2. Product Portfolio and Description
- 12.1.3. Financial Overview
- 12.1.4. Key Developments
12.2. Imagga Technologies Ltd
12.3. Amazon Web Services, Inc
12.4. Qualcomm
12.5. Google LLC
12.6. Microsoft Corporation
12.7. Trax Technology Solutions Pte Ltd
12.8. NEC Corporation
12.9. Ricoh Company, Ltd
12.10. Catchoom Technologies S.L

LIST NOT EXHAUSTIVE