Image Recognition - Worldwide

Image recognition technology has made significant strides in recent years that have been fueled by advancements in deep learning algorithms and the availability of massive amounts of data. Current trends include the use of convolutional neural networks for image classification and object detection, as well as the development of generative adversarial networks for generating realistic images. Other notable trends include the integration of image recognition technology with augmented reality and virtual reality applications, as well as the use of transfer learning to apply pre-trained models to new datasets.

The growth of image recognition technology is being driven by a range of factors, including the increasing availability of image data, the rapid development of computing power, and the growing demand for automation and intelligent systems in various industries. Other contributing factors are the increasing adoption of image recognition technology in areas such as healthcare, retail, and automotive, as well as the rise of edge computing and the deployment of image recognition models on edge devices. A third contributor that is expected to boost demand for this technology is the growing importance of image recognition in fields such as security and surveillance.

The Image Recognition market is expected to continue its growth trajectory in the coming years. According to our market research, the global image recognition market is expected to grow at a compound annual growth rate (CAGR) of 10.4% from 2023 to 2030.

Data coverage: The data encompasses B2B, B2G, and B2C enterprises. Figures are based on the funding values from different industries for the market.

Modeling approach / Market size:Market sizes are determined through a top-down approach with a bottom-up validation, building on a specific rationale for each market. As a basis for evaluating markets, we use annual financial reports, funding data, and third-party data. In addition, we use relevant key market indicators and data from country-specific associations such as GDP, number of internet users, number of secure internet servers, and internet penetration. This data helps us estimate the market size for each country individually.

Forecasts:In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, the S-curve function and exponential trend smoothing are well suited to forecast digital products and services due to the non-linear growth of technology adoption. The main drivers are the level of digitalization, the number of secure internet servers, and the revenue of the Public Cloud market.

Additional Notes: The data is modeled using current exchange rates. The impact of the COVID-19 pandemic and the Russian-Ukraine war are considered at a country-specific level. The market is updated twice a year. In some cases, the market is updated on an ad-hoc basis (e.g., when new, relevant data has been released or significant changes within the market have an impact on the projected development). Data from the Statista Consumer Insights Global survey is weighted for representativeness.