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市場調查報告書

商品編碼

1401307

全球人工智慧影像辨識市場 - 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: 約2個工作天內

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簡介目錄

概述

全球人工智慧影像辨識市場在2022年達到19億美元，預計到2030年將達到46億美元，2023-2030年預測期間CAGR為11.8%。

全球所有行業的自動化趨勢推動了人工智慧影像識別在品質控制、檢查和監控等任務中的應用，這有助於推動人工智慧影像辨識市場的成長。自動化提高了營運效率並減少了對重複性視覺任務的人為干預。人工智慧影像辨識在醫療保健、零售、汽車、安全和農業等多個行業都有應用。影像辨識技術的多功能性有助於其在解決行業特定挑戰方面廣泛採用。

在電子商務和零售業，人工智慧圖像識別為視覺搜尋、產品推薦、庫存管理和客戶參與提供了機會。增強的使用者體驗和個人化服務推動了零售業的採用。智慧城市措施為人工智慧影像辨識在城市規劃、交通管理、公共安全和環境監測方面提供了機會。影像辨識技術的整合有助於高效和永續城市的發展。

AI視覺辨識在北美醫療保健產業的應用日益廣泛，大大助力了AI影像辨識技術的市場拓展。例如，2022 年，美國將 GDP 的近 26% 用於醫療保健設施。在北美，消費者將部分額外現金花在醫療保健上。該地區醫療保健行業的快速成長有助於推動人工智慧圖像識別市場的成長。

動力學

不斷成長的人工智慧採用率

隨著全球對人工智慧技術的認知和理解不斷增強，各行業的企業都認知到將人工智慧圖像識別整合到其營運中的潛在好處。這種理解有助於探索和投資人工智慧解決方案。人工智慧（尤其是深度學習和神經網路）的不斷進步帶來了更複雜和準確的影像辨識模型。技術改進提高了人工智慧影像辨識系統的整體有效性和可靠性，推動了其採用。

根據《哈沃德商業評論》2021 年提供的資料，約 52% 的公司因新冠危機而加速了人工智慧採用計畫。 86% 的參與者表示，人工智慧將在2021 年成為其公司的「主流技術」。哈里斯民調與Appen 合作發現，55% 的公司表示，由於COVID-19，他們在2020 年加快了人工智慧策略，67% 的公司表示，他們在2020 年加快了人工智慧策略。預計在 2021 年進一步加速他們的人工智慧策略。72% 的調查參與者對人工智慧在未來發揮的作用持積極態度。

技術進步

深度學習架構的進步顯著提高了人工智慧影像辨識模型的準確性和效能。增強的演算法和模型架構有助於獲得更精確的影像辨識結果。深度學習的技術進步，特別是更深層神經網路的發展，使人工智慧系統能夠學習影像中複雜的模式和特徵。這導致了影像辨識任務的突破，包括目標偵測和分類。

技術進步使人工智慧影像辨識系統能夠即時處理和分析視覺資料。這對於監控、自動駕駛汽車和即時視訊分析等需要即時決策的應用至關重要。例如，2020 年 3 月 8 日，奧地利光子學研究所的研究人員利用神經網路的光感電子元件在小晶片上創造了人造眼。據報道，在維也納提出的新設計可以在幾納秒內識別物體，這是該技術的重大進步。

AI影像辨識缺乏標準化

如果沒有標準化的實踐，不同人工智慧影像辨識系統之間的互通性就變得具有挑戰性。缺乏相容性阻礙了影像辨識解決方案與現有工作流程和技術的無縫整合。缺乏標準化基準和評估指標導致不同影像辨識模型的表現不一致。這使得企業和用戶很難比較並選擇最適合其特定需求的解決方案。

標準化對於公平、準確地評估不同的人工智慧影像辨識模型至關重要。缺乏標準化的評估標準使得開發者和使用者難以客觀地比較各種模型的表現。標準化在確保人工智慧系統的透明度和可解釋性方面發揮關鍵作用。如果沒有標準化的模型解釋和解釋方法，使用者很難理解人工智慧圖像識別系統如何做出具體決策，從而導致信任問題。

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