Image Recognition Market worth $53.0 billion by 2025, Growing at a CAGR of 15.1% Report by MarketsandMarkets™

As per the report by MarketsandMarkets, the global image recognition market size is projected to reach USD 53.0 billion by 2025, at a CAGR of 15.1% during the forecast period, 2020-2025

Chicago, July 25, 2023 (GLOBE NEWSWIRE) -- The global Image Recognition Market size to grow from USD 26.2 billion in 2020 to USD 53.0 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 15.1% from 2020 to 2025, according to a new report by MarketsandMarkets™. Increased in the need for efficient and profitable retail execution processes with adherence to compliance standards and technological advancements to boost the adoption of image recognition market across the globe. Retailers’ dependency on traditional auditing methods is one of the major challenge of the image recognition market. 

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247 - Tables
45 - Figures
243 - Pages

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Scope of the Report

Report Metrics Details
Market size value in 2020 USD 26.2 Billion
Market size value in 2025 USD 53.0 Billion
Growth rate CAGR of 15.1%
Market size available for years 2014–2025
Base year considered 2019
Forecast period 2020–2025
Forecast units Value (USD Million)
Segments covered By Technology ,Component, Service, Type, Deployment Mode, Application, Vertical and Region
Geographies covered North America, Europe, APAC, MEA, and Latin America
Major companies covered IBM (US), Google (US), Qualcomm (US), Microsoft (US), AWS (US), Trax (Singapore),NEC Corporation (Japan), Catchoom (Spain), Slyce (US), LTU Tech (France), Vispera(Turkey), Blippar(UK), Clarifai(US), Wikitude (Austria), Huawei (China), Honeywell (US) and many more

The report segments the image recognition market by component, by application, by deployment mode, by end user and by region. The component segment is categorized into hardware, solutions and services. The application type segment covered in the market include inventory analysis, product and shelf monitoring analysis and gauging emotions. Further, the deployment type segment is bifurcated into cloud and on premise. The end user segment consists of online and offline. Based on region, the image recognition market is segmented into five major regions: North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America. 

In the component, the services segment is expected to grow at the higher CAGR during the forecast period. Services for the image recognition market include implementation, deployment, product upgrades, maintenance, and consulting. Increase in the adoption of image recognition software has led to the growth of such services, which help organization to increase overall revenue and enhance performance. Vendors in the image recognition market sometimes provide services via channel partners; these partners can help strengthen the geographical reach of solution vendors and improve the cost-effectiveness of their software. The growing demand for rich media is expected to drive companies, such as Partium to offer integrated training services, professional services, and support and maintenance services.    

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In the application type, the gauging emotions segment to grow at a higher CAGR during the forecast period. Image recognition uses the Convolution Neutral Network (CNN) model to capture facial features. Gauging emotions can help companies to understand the customer’s behaviour towards their product. It can also help to place the products on the shelf according to the customers’ demand. Retailers are taking support from their technological partners to implement image recognition technology to gain customer insights that can give them a competitive edge. 

The image recognition market has various opportunities for vendor growth in this market, such as Integration of AI with image recognition solutions to improve store performance and accelerating demand for cloud-powered image recognition solutions to aid representatives in smooth retail execution. Major players in the image recognition market such as Microsoft along with its partners are empowering companies by transitioning from manual operations to AI based operations to optimize retail operations and manage shelf operations efficiently. For example, Clobotics, a Chinese intelligent computer vision provider for retailers, developed a Cloud Image Recognition solution that uses AI, advanced computer vision, and machine learning technologies to provide real-time insights about product placement, shelf optimization, product tracking, and planogram compliance through Microsoft Power BI. Microsoft’s Australia-based partner, Lakeba has combined its computer vision technology with intelligent image capturing, and Microsoft Azure cloud-based data analytics solution to provide optimal on-shelf stock management. The demand for cloud-based services has soared even more due to the outbreak of COVID-19. Intelligence Retail, a computer vision and AI provider for merchandising solutions, leverages the IBM cloud and analytics solution featuring cutting-edge Graphic Processing Units (GPUs) to help organizations to drive sales, decrease audit costs, boost performance, and drive customer loyalty. 

Top Trends in Global Image Recognition Market

  • Artificial Intelligence (AI) Advancements: Deep learning and AI technologies have led to improvements in picture recognition abilities. The accuracy and effectiveness of image recognition systems have increased thanks to better algorithms and neural networks, making them more dependable and usable in a variety of industries.
  • Integration of Image Recognition in Smartphones: The use cases for this technology have grown as a result of the widespread use of smartphones and the integration of image recognition technology into mobile devices. Users are able to carry out a variety of tasks using it, including object recognition, visual search, and augmented reality (AR) applications.
  • Augmented Reality (AR) and Virtual Reality (VR): The demand for image recognition technology has increased as a result of the growing interest in AR and VR applications. For example, facial recognition for avatars or object recognition for interactive features, image recognition is essential to delivering a smooth and interactive experience in AR/VR applications.
  • E-commerce and Retail Applications: Image recognition technology has a wide range of uses in the e-commerce and retail industries. Through the use of photos in visual search, users may locate things online, and augmented reality try-on features make it possible to virtually try on apparel and accessories.
  • Security and Surveillance: To identify and track people, licence plates, and other items, image recognition is becoming more and more used in security and surveillance systems. The technology's ability to detect anomalies and potential threats has made it a valuable tool in safeguarding critical infrastructure and public safety.
  • Healthcare and Medical Imaging: Image recognition is used in the healthcare sector to help doctors diagnose diseases, analyse radiology images, and spot patterns and anomalies in medical scans.
  • Autonomous Vehicles: Image recognition is a key piece of autonomous vehicle technology. To provide safe navigation, cameras and sensors use image recognition algorithms to recognise and interpret road signs, people, cars, and obstructions.
  • Social Media and Content Moderation: Social media platforms use picture recognition to find and control unwanted content, such as hate speech and offensive photographs.
  • Agriculture and Precision Farming: Using precision farming methods, image recognition is used in agriculture to monitor crop health, identify pests and diseases, and improve irrigation and fertilisation practises.
  • Geospatial Analysis: By combining picture recognition and geospatial information, applications like urban planning, environmental monitoring, and catastrophe response are made possible.

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Key Industry Development

  • Integration of AI and Deep Learning: This industrial progress has been largely attributed to the incorporation of artificial intelligence (AI) and deep learning technologies into image recognition systems. These cutting-edge algorithms allow image recognition models to manage complex data, continuously increase accuracy, and adapt to different use cases.
  • Cloud-Based Image Recognition Services: The increased accessibility of image recognition technology is largely due to cloud computing. Scalable solutions are provided by cloud-based image recognition services, enabling enterprises to implement and integrate image recognition capabilities without requiring significant infrastructure investments.
  • Mobile Image Recognition Applications: Applications for mobile image identification have become increasingly popular as a result of the rise of smartphones and other mobile devices. Mobile apps with visual search, augmented reality (AR), and object identification are becoming more and more common, improving user experiences and providing business potential.
  • Edge AI for Real-Time Image Recognition: Edge computing, which processes data closer to the source, has grown significantly in importance. AI at the edge can handle data in real-time and with less latency, which makes it suited for time-sensitive applications like autonomous vehicles and surveillance systems.
  • Cross-Industry Integration: In addition to classic applications, image recognition is being incorporated into a wide range of businesses. Image recognition technology are being adopted by industries like healthcare, agriculture, retail, manufacturing, and automotive to optimise processes.
  • Emphasis on Privacy and Security: As image recognition technology spreads, worries about privacy and security have also risen. With the help of enhanced data protection measures, understandable AI, and ethical frameworks, industry innovations are concentrating on addressing these challenges.
  • Customization and Personalization: Companies are adopting image recognition more and more to give their customers individualised experiences. This includes individualised product recommendations, focused advertising, and material that is developed based on how people engage with images.
  • Hybrid Approaches: To improve performance and versatility across a range of applications, several image recognition solutions are merging different technologies, such as combining deep learning with classic computer vision techniques.
  • Standardization and Interoperability: The industry is working to standardise image recognition protocols and formats, assuring compatibility between various systems and facilitating smooth platform integration.
  • Collaboration and Partnerships: Businesses in the image recognition sector are establishing strategic alliances and partnerships to draw on one another's knowledge and broaden their product lines. These partnerships encourage creativity and hasten the creation of new applications.

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