Research entity Yole Développement (Yole) says autonomous and on-demand processes, for humans and machines alike, are the trends du jour. Outlined in the company’s technology and market report titled “Machine Vision for Industry & Automation”, the transformation inside factories is about automation. And machine vision is the key enabling technology.
Yole forecasts the machine vision camera market will grow from $2 billion in 2017 to roughly $4 billion in 2023, with a 12% CAGR during this period.
The report is a smart combination of market evolution, key figures such as market project and market shares, technology innovations and competitive landscape. It also highlights the diversity of the applications and related technologies. Synergy between the technologies such as image sensors and cameras are also analyzed.
What are the main functions of machine vision in the industry 4.0? What are the technology challenges? Who are the leaders involved in this revolution?
Machine vision for industry and automation is not limited to robotics, but is involved in almost all machines and aspects of the manufacturing cycle.
“The need for quality has boosted machine vision in the automotive, electronic, semiconductor, food, and packaging industries”, asserts Dr Alexis Debray, Technology & Market Analyst at Yole. Food sorting is an interesting example of this push toward automation. The ability to sort fruits and food in general has helped to grow agro-food business revenues, especially in Asian markets. Indeed, machine vision has moved out of the factory. It is now on farms, on roadways for license plate recognition, and more recently in autonomous cars, the market for which we expect to grow at 140% CAGR starting this year.
These dynamics are here to stay. In 2014, the Swatch Group announced its Sistem51, the world first mechanical watch whose manufacturing is fully automated. Since then, other companies have made similar moves, such as Canon in camera manufacturing and Foxconn who announced the deployment of a million robots for manufacturing consumer electronic goods. Sales of automation products are therefore surpassing the growth of industrial production. The trend could accelerate in the future while cheap human labor is scarce in western countries and China is also increasing wages.
“The automation revolution has created a highly dynamic market and player ecosystem”, comments Pierre Cambou, Activity Leader, Imaging at Yole. And he details: “In the three years from 2014 to 2017, M&A activity has accelerated in machine vision both at image sensor and camera level. Among the latest examples of this trend are FLIR acquiring Pointgrey for US$215 million in 2016, and Teledyne acquiring e2v for US$790 million in 2017.”
Yole’s machine vision report mentions other notable acquisitions:
• ams’ acquisition of CMOSIS for US$235 million in 2015
• A little earlier in 2014 On Semi’s consolidation of Aptina for US$400 million and Truesense for US$90 million.
• Most recently, private equity firm Lakesight aggregated machine vision camera makers Tattile, Microtron and Chromasens. The total amount of M&A in this time period is approaching US$1.7 billion.
The shift from CCD to CMOS has had a profound impact on image sensors for machine vision, driving this M&A wave. Although this affected other markets years ago, such as consumer image sensors in the 1990s and photography sensors in the early 2000s, it is only now that the shift from CCD to CMOS technology is reaching the high end of the imaging sensor market. The main direct consequence for companies selling high cost/low volume products is the inability to invest in or sustain manufacturing facilities that require high output due to the large fixed cost. The entry cost to establishing CIS manufacturing has been far too high for previous vertically-integrated CCD players. Therefore, all except Sony have shifted to the fabless business model suited to high-end “specialty products”. Dedicated foundries such as TPSCo, Dongbu Hitek and SMIC, who recently acquired LFoundry’s assets, have therefore emerged.