Skip to main content

How Deep Learning Improves Machine Vision in Factories

Factory automation is evolving, once again. Machine vision technology remains popular in the manufacturing environment, due to its proven track record of results. However, the introduction of artificial intelligence (AI) technology and machine learning applications will transform many conventional factories.

The emergence of deep learning apps creates expanded capabilities and flexibility, leading to more cost efficiency and higher production yield. Deep learning-based machine vision techniques within smart manufacturing will experience a CAGR of 20 percent between 2017 and 2023, with revenue that will reach $34 billion by 2023, according to the latest market study by ABI Research.

Machine Vision Market Development 

Manufacturers need to improve their production yields and workflow efficiency. Legacy machine vision is easy to implement but is somewhat limited. Current solutions that are widely deployed in quality control, safety inspection, predictive maintenance, and industrial monitoring rely upon preprogrammed rules and criteria, supporting limited ranges of functions.

In contrast, AI deep learning-based machine vision is highly flexible due to its ability to be trained and improved using a new set of factory data, enabling manufacturers to incorporate updates and upgrade quickly.

"This is in part driven by the democratization of deep learning capabilities. The emergence of various open source AI frameworks -- such as TensorFlow, Caffe2, and MXNet -- lowers the barrier to entry for the adoption of deep learning-based machine vision," said Lian Jye Su, principal analyst at ABI Research.

These AI frameworks can be deployed using on-premise IT infrastructure and vendor software suites. In the past, the choice of machine vision solutions was limited to a handful of companies that performed relatively simple image processing operations. With deep learning-based machine vision, manufacturers can now develop their own deep learning-based machine vision systems.

In addition to cameras, deep learning-based machine vision can also incorporate data collected from various sensors, including LiDAR, radar, ultrasound, and magnetic field sensors. The rich set of data will provide further insight into other aspects of production processes.

Compared to conventional machine vision, which can only detect product defects and quality issues which can be defined by humans, deep learning algorithms can go even further. These AI algorithms can detect unexpected product defects, providing flexibility and valuable insights to manufacturers.

According to the ABI assessment, manufacturers are encouraged to work with a wide range of vendors, including industrial cloud platform, camera and sensor suppliers, and public cloud vendors. Deep learning-based machine vision requires a robust cloud platform that will enable condition-based monitoring, sensor data collection, and analytics.

Outlook for Machine Vision Application Growth

Unlike conventional machine vision which relies on line-by-line software coding, deep learning-based machine vision models can be deployed by users without significant developer experience, as these models undergo unsupervised learning based on data gathered.

"Manufacturers are opening up to adopting AI capabilities into their workflow. Deep learning-based machine vision will serve as the right catalyst to drive progress. Startups that launch as deep learning-based machine vision solution providers are also beginning to enable big data processing, process optimization, and yield analytics on their platform," concluded Su.

Popular posts from this blog

Global Digital Business and IT Consulting Outlook

Across the globe, CEOs and their leadership teams continue to seek information and guidance about planned Digital Transformation initiatives and the most effective enterprise organization change management practices. Worldwide IT and Business Services revenue will grow from $1.13 trillion in 2022 to $1.2 trillion in 2023 -- that's a 5.7 percent year-over-year growth, according to the latest market study by International Data Corporation (IDC). The mid-term to long-term outlook for the market has also increased -- the five-year CAGR is forecast at 5.2 percent, compared to the previous 4.9 percent. Digital Sevices & Consulting Market Development IDC has raised the growth projection despite a weak economic outlook, because of vendor performances across 2022, growth indicators from adjacent markets, increased government funding, and inflation impacts. The actual 2022 market growth was 6.7 percent (in constant currency), which was 87 basis points higher than forecast last year, alth

Digital Talent Demand Exceeds Supply in Asia-Pac

Even the savviest CEO's desire for a digital transformation advantage has to face the global market reality -- there simply isn't enough skilled and experienced talent available to meet demand. According to the latest market study by IDC, around 60-80 percent of Asia-Pacific (AP) organizations find it "difficult" or "extremely difficult" to fill many IT roles -- including cybersecurity, software development, and data insight professionals. Major consequences of the skills shortage are increased workload on remaining digital business and IT employees, increased security risks, and loss of "hard-to-replace" critical transformation knowledge. Digital Business Talent Market Development Although big tech companies' layoffs are making headlines, they are not representative of the overall global marketplace. Ongoing difficulty to fill key practitioner vacancies is still among the top issues faced by leaders across industries. "Skills are difficul

Mobile Device Market Still Awaiting Recovery

The mobile devices market has experienced three years of unpredictable demand. The global pandemic, geopolitical pressures, supply chain issues, and macroeconomic headwinds have hindered the sector's consistent growth potential. This extremely challenging environment has dramatically affected both demand and supply chains. It has led to subsequent inflationary pressures, leading to a worsening global cost of living crisis suppressing growth and confidence in the sector. In tandem, mobile device industry stakeholders have become more cautious triggering market uncertainties. Mobile Device Market Development Operating under such a backdrop, the development of mobile device ecosystems and vendor landscapes have been impacted severely. Many of these market pressures persisted throughout 2022 and now into 2023, borne chiefly by the smartphone market. According to the latest worldwide market study by ABI Research, worldwide smartphone shipments in 2022 declined 9.6 percent Year-over-Year