Global Deep Learning in Machine Vision
Market Report
2025
The Global Deep Learning in Machine Vision market size will grow at a compound annual growth rate (CAGR) of 55.60% from 2023 to 2030.
The base year for the calculation is 2024. The historical will be 2021 to 2024. The year 2025 will be estimated one while the forecasted data will be from year 2025 to 2033. When we deliver the report that time we updated report data till the purchase date.
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According to Cognitive Market Research, The Global Deep Learning in Machine Vision market size will grow at a compound annual growth rate (CAGR) of 55.60% from 2023 to 2030.
2021 | 2025 | 2033 | CAGR | |
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Global Deep Learning in Machine Vision Market Sales Revenue | 121212 | 121212 | 121212 | 55.6% |
Base Year | 2024 |
Historical Data Time Period | 2021-2024 |
Forecast Period | 2025-2033 |
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Market Split by Application |
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Market Split by Vertical |
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List of Competitors |
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Regional Analysis |
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Country Analysis |
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Deep Learning in Machine Vision Market is Segmented as below. Particular segment of your interest can be provided without any additional cost. Download the Sample Pages!
Deep learning in machine vision systems are those systems that can see and understand the world around them just like humans; those are the computers capable of understanding digital images and videos. Furthermore, the growth of deep learning in the machine vision market is increasing the adoption of AI, deep learning and technical advancements in hardware and software. The rising adoption of 3D inspection systems over conventional inspection systems is increasing, boosting the market's growth.
These developments empower businesses to offer better-tailored solutions and services, which, in turn, contribute to the growth of deep learning in the machine vision industry.
For instance,Cadence Design Systems, Inc. has announced its widely acclaimed Tensilica Vision DSP product line by introducing two new DSP IP cores catering to embedded vision and AI. The leading-edge Cadence Tensilica Vision Q8 DSP boasts an impressive 3.8 tera operations per second (TOPS), delivering twice the performance and memory bandwidth of its predecessor, the Tensilica Vision Q7 DSP.
One of the primary drivers of Deep Learning in the Machine Vision Market is the continuous advancements in deep learning algorithms. Deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have demonstrated impressive abilities in detecting and identifying images and patterns. These algorithms are becoming increasingly sophisticated, enabling machines to accurately process and interpret visual data.
Microsoft has recently unveiled the next stage of its extensive collaboration with OpenAL. This long-term partnership involves a substantial investment spanning multiple years and billions of dollars. The primary objective is to expedite advancements in Al technology, with the ultimate goal of making these benefits accessible on a global scale.
(Source:blogs.microsoft.com/blog/2023/01/23/microsoftandopenaiextendpartnership/)
Large datasets that may be used to train deep neural networks have created new opportunities in a variety of fields, including autonomous cars, medical image analysis, and industrial quality control. Deep learning algorithms are improving the accuracy and speed of machine vision systems, which makes them more adaptable and appealing to a variety of sectors.
The growing demand for automation across industries. As businesses seek to improve operational efficiency, reduce production costs, and maintain consistent product quality, they turn to machine vision systems empowered by deep learning. These systems have the ability to carry out intricate functions, like identifying defects, recognizing objects, and analyzing gestures, at a speed and magnitude that exceeds what humans can achieve. Industries like automotive, electronics, and logistics are increasingly integrating deep learning-based machine vision solutions into their processes to ensure accuracy, minimize errors, and achieve higher productivity. The demand for automation, driven by factors like labor shortages, rising labor costs, and the need for 24/7 operations, is a significant growth driver in this market.
Data privacy and security issues significantly restrain the Deep Learning in Machine Vision Market. Deep learning algorithms rely heavily on vast datasets for training and continuous learning. However, collecting, storing, and utilizing sensitive visual data can raise concerns, especially in applications involving personal information, such as healthcare and surveillance. Ensuring compliance with data protection regulations, like HIPAA and GDPR, becomes a complex challenge for companies implementing machine vision systems. There is a growing need to balance leveraging machine vision for its advantages and safeguarding individuals' privacy rights. Addressing these concerns with robust data anonymization and encryption measures is vital to building trust and sustaining the market's growth. Additionally, the risk of data breaches and unauthorized access remains challenging, requiring continuous vigilance in an evolving threat landscape.
The COVID-19 pandemic significantly impacted the Deep Learning in Machine Vision Market. While the market was experiencing steady growth before the pandemic, the global health crisis accelerated the adoption of machine vision systems powered by deep learning algorithms. The need for automation and contactless solutions in various industries, including healthcare, manufacturing, and retail, became more pronounced during the pandemic. Deep learning-based machine vision technologies enabled quality control, monitoring, and social distancing enforcement tasks. As a result, many businesses invested in these technologies to enhance operational efficiency and reduce human intervention, thereby driving the market's growth. The pandemic catalyzed the wider acceptance and implementation of deep learning in machine vision, setting the stage for continued expansion in the post-pandemic era.
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The machine vision market is characterized by fierce competition and diversity in deep learning. This sector encompasses many companies, varying from large multinational corporations to smaller niche businesses, all vying to provide innovative solutions, proficient translation and interpretation, and advanced deep learning technology. Globalization and advancements in technology drive such competition.
(Source:newsroom.ibm.com/2023-01-18-IBM-and-MBZUAI-Advance-AI-for-Climate-and-Culture)
Top Companies Market Share in Deep Learning in Machine Vision Industry: (In no particular order of Rank)
If any Company(ies) of your interest has/have not been disclosed in the above list then please let us know the same so that we will check the data availability in our database and provide you the confirmation or include it in the final deliverables.
According to Cognitive Market Research, North America, specifically the United States, has emerged as the dominant region in the Deep Learning in Machine Vision market due to a combination of key factors. The United States has a strong technological innovation and entrepreneurship ecosystem. Silicon Valley, in particular, is a global hub for cutting-edge research and development in artificial intelligence and deep learning, attracting top talent and substantial investment in machine vision technologies. This fosters a dynamic environment for innovation. The United States has a mature market for technology adoption. Industries such as healthcare, automotive, and manufacturing have eagerly embraced machine vision to enhance productivity and efficiency, driving significant growth in the sector. The United States benefits from a well-established network of leading universities, research institutions, and tech companies that actively collaborate on advancing deep learning techniques. This ensures a continuous flow of talent and breakthroughs.
The Asia-Pacific region's Deep Learning in Machine Vision market is experiencing rapid growth for various reasons. The region is home to some of the world's largest and fastest-growing economies, including China, India, and South Korea. These countries have been actively investing in technology infrastructure and manufacturing sectors, which has resulted in a higher demand for machine vision solutions. Industries such as electronics, automotive, and manufacturing increasingly rely on these solutions to improve automation, quality control, and operational efficiency. The Asia-Pacific region benefits from a large and highly skilled engineering and computer science workforce. This well-equipped workforce is actively contributing to developing and implementing deep learning in machine vision technologies.
The current report Scope analyzes Deep Learning in Machine Vision Market on 5 major region Split (In case you wish to acquire a specific region edition (more granular data) or any country Edition data then please write us on info@cognitivemarketresearch.com
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Global Deep Learning in Machine Vision Market Report 2025 Edition talks about crucial market insights with the help of segments and sub-segments analysis. In this section, we reveal an in-depth analysis of the key factors influencing Deep Learning in Machine Vision Industry growth. Deep Learning in Machine Vision market has been segmented with the help of its Offering, Application Object, and others. Deep Learning in Machine Vision market analysis helps to understand key industry segments, and their global, regional, and country-level insights. Furthermore, this analysis also provides information pertaining to segments that are going to be most lucrative in the near future and their expected growth rate and future market opportunities. The report also provides detailed insights into factors responsible for the positive or negative growth of each industry segment.
According to Cognitive Market Research, the software segment is both the dominant and fastest-growing. In order to build, train, and deploy neural networks for tasks like image recognition, object identification, and pattern analysis, deep learning significantly relies on sophisticated software. As the demand for machine vision solutions across industries like automotive, healthcare, manufacturing, and agriculture continues to surge, so does the need for powerful and versatile software tools. These software solutions include neural network frameworks, pre-trained models, and user-friendly development environments. They offer the flexibility to adapt to diverse use cases, making them a fundamental component of any machine vision system. Moreover, software providers continually update and enhance their offerings, staying at the forefront of technological advancements and keeping them at the market's helm.
The hardware segment has emerged as the fastest-growing segment.The hardware segment is the continuous technological advancements in machine vision hardware components. These hardware components include specialized GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), and custom-designed AI accelerators. These advanced hardware components are purpose-built to handle the intensive computational requirements of deep learning algorithms, making them indispensable for high-performance machine vision applications. As deep learning models become more complex and sophisticated, businesses and organizations increasingly invest in powerful hardware to ensure faster processing speeds and improved accuracy.
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The inspection application segment dominates in the Deep Learning in Machine Vision Market because of its wide-ranging applications across various industries. Inspection tasks involve examining products, components, and materials for defects, quality control, and adherence to specifications. Deep learning in machine vision has revolutionized inspection processes by enabling high-speed, accurate, and automated defect detection. Industries such as manufacturing, automotive, electronics, and pharmaceuticals heavily rely on machine vision inspection systems to ensure product quality, reduce production errors, and enhance efficiency.
According to Cognitive Market Research, THE object classification application segment is experiencing rapid growth primarily due to its relevance in various sectors, including e-commerce, autonomous vehicles, and robotics. Furthermore, object classification is vital for autonomous driving systems in the automotive industry. Vehicles equipped with deep learning-based vision can recognize and classify objects on the road, enhancing safety and navigation.
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According to Cognitive Market Research, the dominance of the image segment in the Deep Learning in Machine Vision Market can be attributed to its fundamental role in machine vision processes and its extensive range of applications across various industries. Image-based deep learning encompasses examining and understanding static images, making it a vital element for numerous applications. Furthermore, image-based machine vision is exceptionally adaptable and useful in diverse sectors such as manufacturing, healthcare, automotive, agriculture, etc. It is employed for quality control, defect detection, pattern recognition, and other purposes.
The video section is experiencing rapid growth in the deep learning in machine vision market. Video-based machine vision plays a crucial role in real-time surveillance and monitoring applications by enabling constant tracking of objects, individuals, or events in a dynamic setting. Additionally, autonomous vehicles and drones heavily depend on video-based machine vision for navigation, obstacle detection, and object recognition. With the increasing prominence of autonomous technologies, this sector is expected to experience substantial growth. Furthermore, video analytics in retail stores offer valuable insights into customer behavior, foot traffic, and store layout optimization, thereby contributing to the expansion of this segment.
According to Cognitive Market Research, the manufacturing sector is prominent in the Deep Learning in Machine Vision Market because it traditionally relies on automation and machine vision technologies. Industries such as electronics, automotive, and food and beverages greatly depend on deep learning in machine vision to ensure quality control and detect defects. These businesses may make sure that their goods fulfil high quality requirements by utilising automated inspection systems. Additionally, machine vision technology improves manufacturing efficiency and productivity by identifying bottlenecks, minimizing waste, and enhancing overall production processes.
The healthcare sector is experiencing rapid growth in deep learning in the machine vision market. This technology transforms medical imaging, enabling more accurate and efficient analysis of X-rays, MRIs, CT scans, and pathology slides. Advanced algorithms support radiologists and pathologists in diagnosing diseases with greater precision and speed. Additionally, machine vision is crucial in early disease detection, particularly in cancer screening. Identifying anomalies and potential health concerns in medical images facilitates timely interventions and enhances patient outcomes.
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The Global Deep Learning in Machine Vision Market is witnessing significant growth in the near future.
In 2023, the Hardware segment accounted for noticeable share of global Deep Learning in Machine Vision Market and is projected to experience significant growth in the near future.
The Inspection segment is expected to expand at the significant CAGR retaining position throughout the forecast period.
Some of the key companies Cognex Corporation , NATIONAL INSTRUMENTS CORP. and others are focusing on its strategy building model to strengthen its product portfolio and expand its business in the global market.
Please note, we have not disclose, all the sources consulted/referred during a market study due to confidentiality and paid service concern. However, rest assured that upon purchasing the service or paid report version, we will release the comprehensive list of sources along with the complete report and we also provide the data support where you can intract with the team of analysts who worked on the report.
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Offering | Hardware, Software , Services |
Application | Inspection, Image Analysis, Anomaly Detection, Object Classification, Object Tracking, Counting, Bar Code Detection, Feature Detection, Location Detection, Optical Character Recognition, Face Recognition, Instance Segmentation, Others |
Object | Image , Video |
Vertical | Electronics, Manufacturing, Automotive and Transportation, Food & Beverages, Aerospace, Healthcare, Building and Material, Power, Others |
List of Competitors | Cognex Corporation , Intel Corporation , NATIONAL INSTRUMENTS CORP. , SICK AG, Datalogic S.p.A. , STEMMER IMAGING AG , Abto Software, Adaptive Vision Sp. z o.o., Autonics Corporation, Basler AG, Cyth Systems Inc., EURESYS S.A., IDS Imaging Development Systems GmbH , Integro Technologies Corp., LeewayHertz, Matrox Imaging , MVTEC SOFTWARE GMBH, Omron Microscan Systems Inc. , perClass BV , Qualitas Technologies , RSIP Vision, USS Vision LLC, Viska Automation Systems Ltd. , T/A Viska Systems |
This chapter will help you gain GLOBAL Market Analysis of Deep Learning in Machine Vision. Further deep in this chapter, you will be able to review Global Deep Learning in Machine Vision Market Split by various segments and Geographical Split.
Chapter 1 Global Market Analysis
Global Market has been segmented on the basis 5 major regions such as North America, Europe, Asia-Pacific, Middle East & Africa, and Latin America.
You can purchase only the Executive Summary of Global Market (2019 vs 2024 vs 2031)
Global Market Dynamics, Trends, Drivers, Restraints, Opportunities, Only Pointers will be deliverable
This chapter will help you gain North America Market Analysis of Deep Learning in Machine Vision. Further deep in this chapter, you will be able to review North America Deep Learning in Machine Vision Market Split by various segments and Country Split.
Chapter 2 North America Market Analysis
This chapter will help you gain Europe Market Analysis of Deep Learning in Machine Vision. Further deep in this chapter, you will be able to review Europe Deep Learning in Machine Vision Market Split by various segments and Country Split.
Chapter 3 Europe Market Analysis
This chapter will help you gain Asia Pacific Market Analysis of Deep Learning in Machine Vision. Further deep in this chapter, you will be able to review Asia Pacific Deep Learning in Machine Vision Market Split by various segments and Country Split.
Chapter 4 Asia Pacific Market Analysis
This chapter will help you gain South America Market Analysis of Deep Learning in Machine Vision. Further deep in this chapter, you will be able to review South America Deep Learning in Machine Vision Market Split by various segments and Country Split.
Chapter 5 South America Market Analysis
This chapter will help you gain Middle East Market Analysis of Deep Learning in Machine Vision. Further deep in this chapter, you will be able to review Middle East Deep Learning in Machine Vision Market Split by various segments and Country Split.
Chapter 6 Middle East Market Analysis
This chapter will help you gain Middle East Market Analysis of Deep Learning in Machine Vision. Further deep in this chapter, you will be able to review Middle East Deep Learning in Machine Vision Market Split by various segments and Country Split.
Chapter 7 Africa Market Analysis
This chapter provides an in-depth analysis of the market share among key competitors of Deep Learning in Machine Vision. The analysis highlights each competitor's position in the market, growth trends, and financial performance, offering insights into competitive dynamics, and emerging players.
Chapter 8 Competitor Analysis (Subject to Data Availability (Private Players))
(Subject to Data Availability (Private Players))
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
Data Subject to Availability as we consider Top competitors and their market share will be delivered.
This chapter would comprehensively cover market drivers, trends, restraints, opportunities, and various in-depth analyses like industrial chain, PESTEL, Porter’s Five Forces, and ESG, among others. It would also include product life cycle, technological advancements, and patent insights.
Chapter 9 Qualitative Analysis (Subject to Data Availability)
Segmentation Offering Analysis 2019 -2031, will provide market size split by Offering. This Information is provided at Global Level, Regional Level and Top Countries Level The report with the segmentation perspective mentioned under this chapters will be delivered to you On Demand. So please let us know if you would like to receive this additional data as well. No additional cost will be applicable for the same.
Chapter 10 Market Split by Offering Analysis 2021 - 2033
The report with the segmentation perspective mentioned under this chapters will be delivered to you On Demand. So please let us know if you would like to receive this additional data as well. No additional cost will be applicable for the same.
Chapter 11 Market Split by Application Analysis 2021 - 2033
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Chapter 12 Market Split by Object Analysis 2021 - 2033
The report with the segmentation perspective mentioned under this chapters will be delivered to you On Demand. So please let us know if you would like to receive this additional data as well. No additional cost will be applicable for the same.
Chapter 13 Market Split by Vertical Analysis 2021 - 2033
This chapter helps you understand the Key Takeaways and Analyst Point of View of the global Deep Learning in Machine Vision market
Chapter 14 Research Findings
Here the analyst will summarize the content of entire report and will share his view point on the current industry scenario and how the market is expected to perform in the near future. The points shared by the analyst are based on his/her detailed in-depth understanding of the market during the course of this report study. You will be provided exclusive rights to interact with the concerned analyst for unlimited time pre purchase as well as post purchase of the report.
Chapter 15 Research Methodology and Sources
Why Hardware have a significant impact on Deep Learning in Machine Vision market? |
What are the key factors affecting the Hardware and Software of Deep Learning in Machine Vision Market? |
What is the CAGR/Growth Rate of Inspection during the forecast period? |
By type, which segment accounted for largest share of the global Deep Learning in Machine Vision Market? |
Which region is expected to dominate the global Deep Learning in Machine Vision Market within the forecast period? |
Segmentation Level Customization |
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Region level Data Customization |
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Country level Data Customization |
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Company Level |
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Additional Data Analysis |
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Additional Qualitative Data |
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Additional Quantitative Data |
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Service Level Customization |
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