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

Why Healthcare and Smart City Apps Drive 5G IoT

Fifth-generation (5G) wireless technology for cellular networks is a successor to fourth-generation (4G) wireless technology. By 2023, Juniper Research anticipates that there will be over 1 billion 5G connections globally. The technology will provide the data infrastructure for the advancement of wireless communications and for new developments in the Internet of Things (IoT) -- including smart cities and healthcare. 5G IoT Market Development According to the latest worldwide market study by Juniper Research, 5G IoT connections will reach 116 million globally by 2026 -- that's increasing from just 17 million connections in 2023. Juniper analysts predict that the healthcare sector applications and government or other smart city services will drive this outstanding 1,100 percent growth over the next three years. Juniper examined 5G adoption across key industry sectors -- such as the automotive, mobile broadband, and smart homes -- and forecasts healthcare and smart cities will accoun

How Savvy Leaders Re-Imagine Work in 2023

As we look to the year ahead, there will be significant challenges and opportunities facing the Chief Human Resource Officer (CHRO) role. In order to be successful, savvy HR leaders must be prepared to take proactive steps that adapt and evolve. "HR leaders have faced an increasingly unpredictable environment amid many organizations mandating a return to office, permanently higher turnover and burnt out employees," said Emily Rose McRae, senior director at Gartner . HR Innovation Market Development One of Gartner's key predictions for 2023 is that the use of artificial intelligence (AI) and automation will continue to increase within the enlightened digital workplace. This transition will require HR leaders to develop new skills and competencies in order to effectively manage and lead teams that are increasingly relying on these enabling technologies. Additionally, HR leaders will need to ensure that their organizations are investing in the necessary infrastructure and re

Top 10 CFO Priorities Require Rethinking Finance

The Chief Financial Officer (CFO) role is essential to digital business growth. While CFOs do not get closely involved in the tactical details of the digital transformation of their functions, they still recognize its strategic importance. According to the latest survey by Gartner, CFOs are faced with the challenge of balancing the need for substantive digital business innovation with financial cost control and risk management. "CFOs will be stretched thinly across many activities in 2023. The survey revealed a wide range of actions CFOs plan to either lead or be significantly involved with," said Marko Horvat, vice president at Gartner. Survey Findings: The Top Ten Priorities Cost Optimization - Cost reduction remains the top priority for CFOs as they look for ways to cut costs and improve efficiency in their operations. This includes identifying cost-saving opportunities through automation, outsourcing, and business process improvement. Business Continuity - The global C