Core technology drivers such as electronic miniaturization, advancements in camera technologies and imaging techniques as well as enhanced processing power drive the computer vision market. Vision systems assist with the achievement of manufacturing efficiencies, regulatory compliance and the avoidance of costs associated with defective products.
New analysis from Frost & Sullivan, Advances in Machine Vision Systems, finds that machine vision systems are becoming critical to the emerging requirements of industrial automation and robotics. These systems add a new dimension to statistical quality control principles and process control systems. In addition, advancements in semiconductors and reductions in computing costs have provided an impetus to the deployment of machine vision systems.
Cost-effective manufacturing in developed economies starts with eliminating human labor wherever possible to reduce the high cost of labor. Furthermore, quality control processes exhibit lower reliability due to human error which can further increase costs. Today’s manufacturing industry has a zero defect tolerance which necessitates the utilization of machine vision systems.
Vision systems now incorporate user-friendly features that minimize operator training thereby resulting in considerable cost benefits for the end user. As quality expectations continue to rise, the implementation of machine vision systems appears inevitable.
Additionally, the regulatory compliance standards in the food and pharmaceutical industries drive the advancement of machine vision technologies. Overall, the prevention of liability due to defective products makes the deployment of vision systems very attractive.
“Machine vision systems are increasingly used for complex factory applications due to advancements in machine vision components such as cameras, illumination systems, processors, and imaging techniques,” notes Frost & Sullivan Research Analyst Vishnu Sivadevan. “Experience gained in solving challenging inspection tasks enables machine vision integration to progress further.”
However, due to the availability of low-cost labor in developing economies, machine vision systems remain confined to high-end or large-scale manufacturers as they represent a significant cost for small-scale and medium-scale manufacturers. On the operational level, vision systems entail a high cost of development and deployment while posing challenges for future upgrades.
Vision systems must also demonstrate a consistently robust performance that overcomes changes or variations in its operating environment. For instance, machine vision systems should adapt to various ambient light conditions in factory environments as well as offer a fair degree of flexibility for changes in specifications of inspected products.
“Pre-deployment costs and cost of ownership are major factors that must be considered to ensure a high return on investment,” notes Sivadevan. “Continuous maintenance and operator training support are essential for high-end machine vision systems.”
Apart from cost-effective deployment, maintenance and upgradeability of machine vision systems, integrators have to cater to the rapid changes in manufacturing environments by providing continuous support for end users’ evolving needs.
Currently, machine vision enables core application areas such as semiconductor manufacturing inspection processes. The number of potential applications for machine vision continues to increase with the rate of innovation.
Advances in Machine Vision Systems is part of the Technical Insights Growth Partnership Service program, and provides a technology overview and outlook for the machine vision systems industry. The study covers architectures by which vision systems are built, machine vision cameras, advanced imaging and image processing techniques, infrared imaging, robotics, and artificial intelligence and vision. This research service includes detailed technology analysis and industry trends evaluated following extensive interviews with market participants. Interviews with the press are available.
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