Similar in complexity to voice recognition, machine vision involves the ability of a computer to see, employing one or more video cameras, analog-to digital conversion (ADC) and digital signal processing (DSP). The data produced goes to a computer or robot controller. Computer vision algorithms process the data before employing other system components to act upon that data.
Machine vision approaching milestone
Machine vision is a mature technology with established incumbents. However, significant advancements in chipsets, software, and standards are bringing deep learning innovation into the machine vision sector. According to a recent analysis by ABI Research, total shipments for machine vision sensors and cameras will reach 16.9 million by 2025, creating an installed base of 94 million machine vision systems in industrial manufacturing. Of that installed base, 11% will be deep learning-based.
A different breed from conventional machine vision technology, deep learning-based machine vision is data-driven and utilizes a statistical approach, which allows the machine vision model to improve as more data is gathered for training and testing. Major machine vision vendors have realized the potential of deep learning-based machine learning. Cognex, for example, acquired SUALAB, a leading Korean-based developer of vision software using deep learning for industrial applications, and Zebra Technologies acquired Cortexica Vision Systems Ltd., a London-headquartered leader in business-to-business (B2B) AI-based computer vision solutions developer.1
2D, 3D machine vision systems market forecast
A report by Reportlinker predicts that the 2D and 3D vision systems market will grow at a CAGR of more than 15 percent between 2019 and 2024. The report says that inspection needs amid growth in automation are translating into increased adoption of vision technology. The report cites general categories of applications in robotics, dimensional gaging operations, assembly verification, flaw detection, paint job verification and code reading. The report mentions Europe in a leading position in the global market, through the influence many small and medium-sized innovative companies, as well as a network of research centers spread across the continent, suck as IMEC of Belgium, Fraunhofer Institutes of Germany, Wageningen University and TENO universities of Netherlands, and CVC and AIDO of Spain.2
Machine vision eyed for counterfeit electronics detection
Defense Microelectronics Activity (DMA) , an organization within the U.S. Department of Defense that provides microelectronic components and assemblies for legacy systems, has awarded contracts to Dr. Michael Azarian and Dr. Diganta Das of Maryland’s Center for Advanced Life Cycle Engineering (CALCE) to test how machine vision-based imaging technologies can be employed to detect counterfeit microelectronic components, and evaluate conventional methods for counterfeit detection, in an effort to improve integrity of the supply chain for integrated circuits. “Machine-vision detection technology includes systems that leverage side-channels (also known as second-order effects) and/or machine learning algorithms to assess the authenticity of a microelectronic device,” CALCE said in a statement.
A U.S.-based electronics distributor was convicted last year and sentenced to close to four years in prison for selling counterfeit integrated circuits that ended up in a classified weapons system, according to the Department of Justice.3
Robot, do you see what I see?
Researchers at the Massachusetts Institute of Technology are engaged in a study to create a more robust machine vision architecture by studying how the human brain remembers and recognizes objects despite changing viewpoints and conditions. The study, reported in a paper by MIT PhD candidate in electrical engineering and computer science Yena Han and colleagues in Nature Scientific Reports entitled “Scale and translation-invariance for novel objects in human vision” discusses how they study this phenomenon more carefully to create novel biologically inspired networks. The paper is co-authored by Tomaso Poggio — director of the Center for Brains, Minds and Machines (CBMM) and the Eugene McDermott Professor of Brain and Cognitive Sciences at MIT. “Our work provides a new understanding of the brain representation of objects under different viewpoints. It also has implications for AI, as the results provide new insights into what is a good architectural design for deep neural networks,” remarks Han, CBMM researcher and lead author of the study.4
References
1. Globe Newswire, “The 2D and 3D machine vision systems market is expected to grow significantly at a CAGR of over 15% during the forecast period 2019,” Feb. 27, 2020
2. Securing Industry, “US backs machine-vision pilot for fake microelectronics detection,” March 4, 2020
3. mit.edu, “Bridging the gap between human and machine vision,” Feb. 11, 2020
http://news.mit.edu/2020/bridging-gap-between-human-and-machine-vision-0211
4. IndustryWeek.com, “Machine vision approaching milestone,” Feb. 18, 2020