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

Digital Solutions for Industrial & Manufacturing Firms

Executive leaders of fast-moving consumer goods (FMCG) are seeking guidance on how to apply new business technology in their manufacturing operations. CIOs and CTOs are tasked with gaining insight into the best solutions for digital transformation. ABI Research evaluated the impact politics, regulation, the economy, supply chain, ESG, and technology are having on FMCG, pharma, producers of steel, chemicals, pulp and paper -- as well as the mining and oil & gas sectors. Digital Transformation Market Development "Our assessment found that the FMCG sector is under pressure from all sides," says Michael Larner, industrial & manufacturing research director at ABI Research . Securing raw materials is challenging considering lockdowns in China and limited grain supplies from Ukraine. Supply shocks are raising input costs, and operating costs are rising with higher energy costs coupled with the pressure to pay higher wages and work sustainably. "We all hoped that with th

5G Fixed Wireless Access Revenue to Reach $24B

Available Internet access at an affordable cost is essential for everyone to participate in the Global Networked Economy. The deployment of fifth-generation (5G) wireless communications infrastructure is enabling the introduction of lower-cost broadband services in some markets. Fixed Wireless Access (FWA) allows mobile network operators (MNO) to deliver high-speed Internet connections in areas that have either insufficient or no prior wireline broadband access services. It's also used in urban, suburban, and rural areas where fiber optic communication is considered too expensive to install and maintain. With this new technology, MNOs have the potential to provide broadband capability at similar levels to fiber optic networks. Fixed Wireless Access Market Development Therefore, FWA can be used to supplement existing wired broadband Internet service offerings, provide additional broadband capacity, or act as a backup service for home or business applications. Although FWA is well es

Why the C-Suite Craves Digital App Acceleration

Business model evolution and growth are still top priorities for forward-thinking leadership. In fact, 70 percent of surveyed boards of directors will accelerate digital business initiatives, steering the organization to digitally-enabled growth. Chief Financial Officers (CFOs) also plan to protect their digital transformation investments as they cut costs elsewhere in their operations, according to the latest market study by Gartner. Among technology priorities, CFOs have particularly prioritized back-office business automation technology as a key to driving down costs in the face of ongoing inflation and supply chain challenges. Digital Applications Market Development A survey of CFOs found that digital business app acceleration was the top spending priority over the next 12 months, with 98 percent of respondents saying they will protect digital investments. Meanwhile, 66 percent of surveyed CFOs said they plan to increase their digital app investments. A separate survey of CEOs high