Automatic inspection by machine vision is leading to the dawn of the “smart factory,” eliminating errors resulting from human manual operations, improving quality consistency, increasing productivity, reducing production costs, and enhancing customer satisfaction. With consideration for limited floor space and budgeting for installation, small, compact, integrated, easily installed vision systems are drawing more interest from operators who are looking for a competitive advantage, and these systems have seen annual sales growth of 30% in recent years.

With consideration for limited floor space and budgeting for installation, small, compact, integrated, easily installed vision systems are drawing more interest from operators who are looking for a competitive advantage, and these systems have seen annual sales growth of 30% in recent years.

Multiple solution options—with their own advantages and disadvantages—currently exist for machine vision applications. Embedded vision systems provide great computing performance, but with a larger footprint, more complex deployment, and higher price tag. Industrial smart cameras offer a compact size and fanless operation, but require a lower power, lower performance ARM-based CPU with limited memory—and therefore limited imaging capabilities. However, the convergence of high performance and low power consumption on new processors has opened up the possibility for an industrial smart camera solution that offers the best features from both larger embedded systems and smaller conventional smart cameras, providing a new alternative for machine vision applications.

What makes a factory smart?

To understand the elements critical to a machine vision system, one must first understand the many operating requirements of the smart factory.

High efficiency and throughput: System efficiency and throughput is critical for the higher productivity most industrial manufacturers pursue. However, there is a cost. In terms of conventional machine vision systems, high resolution and high frame rate are hard to achieve at the same time.

In reality, vision applications are often a marriage of high resolution with lower frame rate, or lower resolution with higher frame rate. To achieve both, a more advanced CPU is needed with costs raised commensurately. Striking the delicate balance necessary among these factors and achieving optimal efficiency with reasonable cost structure is an important issue faced by system developers on an ongoing basis.

Ruggedness and reliability: Operating environments of industrial production are often challenging for automatic systems. For example, a food and beverage production facility is likely to present damp conditions with extreme temperatures, while machine tooling environments are often dusty with metal or other intrusive particulates present. If the vision system is to be installed adjacent to production equipment, a higher degree of imperviousness to such elements is needed.

Integration with 3rd party equipment:
A production line usually involves a series of operations from manufacturing, machining, pick and place, and inspection to packaging. For instance, in CNC turning operations, a number of different machines are used with an external controller, such as conveyors or robotic arms to move components from machine to machine and align them under the guidance of industrial cameras before cutting operations commence. After turning, the objects are conveyed to the next operation stand for flaw inspection. Finally, approved products are sent to packaging and undergo barcode reading for shipping. Integration of and communication among the different systems involved is a challenge for all smart factories.

Faster development:
Development of software solutions and related compatibility issues are critical factors, dictating success or failure of the implementation. Shortening development time and reducing system development costs are distinct challenges.

Types of vision systems

As mentioned, conventional industrial smart cameras are small, compact, all-in-one vision systems that incorporate lens, image sensors, system storage and processors into a single device, a combination of camera and computer.

Theoretically, more powerful processors are capable of executing more complex tasks at higher speeds. However, they also draw more power and require larger physical size to accommodate heat dissipation measures, with fan use frowned upon with respect to rugged requirements; moving parts can easily fail.

Thus, conventionally, smart cameras have made use of a low power ARM-based or single-core microprocessor with limited memory, in order to meet requirements for size and ruggedness. This minimal memory limits the camera’s ability to process high-resolution images with sufficient speed for most industrial processes, and also impedes multi-tasking. Accordingly, conventional smart cameras are often single-purposed, dedicated to simpler image tasking—such as gaging, counting, alignment, or barcode scanning. Due to their minimal expandability, realization of additional functions requires installation of more system units.

When more complex, high performance machine vision applications are to be undertaken and expandability is demanded, users often turn to the other category of vision system mentioned in the intro, embedded vision systems, comprised of an industrial PC connected to high-resolution industrial cameras. Embedded vision systems typically feature a high-performance processor running a standard PC operating system with multiple vision channels supported to deliver a full set of image processing functions. Rich and versatile I/O connectivity allows flexible connection with other field devices in the factory.

Embedded vision systems are, however, often more costly and complicated to deploy. They also have a larger footprint compared with smart cameras, which makes usage limited on space-constrained production floors. More cables and possible use of fans also affects system reliability.

How to select the right
solution for your machine vision application

The following are implementation factors to consider when selecting your machine vision solution:

CPU efficiency:
As mentioned, conventional smart cameras usually run on a single-core Atom processor or ARM-based processor with considerations of size, power and heat dissipation. However, these conventional smart cameras have limited computing power and are often used only in simple image applications dealing with individual tasking of gaging, counting, alignment, or barcode scanning.

Image sensors:
Image sensors are the eyes of the vision systems. Larger sensors can acquire more image information and deliver higher image quality. In the past, with conventional smart cameras focused on simple imaging tasks, the size of image sensors was not an issue. With, however, the implementation of high-end and high-speed applications, image sensor size becomes critical for image quality.

Rolling shutter vs. global shutter: Rolling shutters and global shutters differ in the way their pixels collect light. Rolling shutters collect light in sequential rows, with each row starting and finishing collection slightly different from each other. Global shuttering pixels start and end light collection during exactly the same period of time.

Conventional smart cameras, because of limited computing power insufficient to process large amounts of image data, have tended to adopt rolling shutter function. Even so, the inability of rolling shutters to remove residual signals when dealing with fast-moving objects (with attendant blur/skew/wobble/partial exposure effects), has excluded conventional smart cameras from use in high-speed industrial applications.

Currently, however, with the improved CPU efficiency of new generation processors, small form factor smart cameras are able to support global shutter deployment.

Co-processor:
While image quality is critical for accurate automatic inspection and analysis, limits of optical conditions (light source or lens) frequently cause acquired images to exhibit inconsistent brightness, leading to misjudgment in analysis. If the vision system can automatically optimize acquired images before submission for analysis, accuracy of image analysis is significantly enhanced.

In conventional vision systems, captured image data is processed by CPU. When processor resources are insufficient, the amount of image data able to be processed is reduced. Thus, conventional smart cameras must frequently compromise either image resolution or frame rate.

The use of an FPGA co-processor by new generation smart cameras greatly improves image processing efficiency by offloading image matrix operations from CPU to FPGA (image pre-processing), freeing CPU resources to carry out more advanced algorithmic operations. The FPGA co-processor can carry out image pre-processing tasks such as LUT (look up table), ROI (region of interest) and shading correction, with smaller vision systems accordingly realizing faster and more complex applications.

Graphics and media processing: New generation processors adopt a GPU driver, which offloads media processing tasks from the CPU, tripling graphic processing performance over previous generation processors. The GPU can process video encoding, compression and transmission across multiple channels simultaneously. This performance improvement empowers small vision systems to record, store and analyze media data, resulting in a “smarter” factory.

Display:
Conventional smart cameras transmit data via only an Ethernet cable connected to the control center. If the vision system can also connect with HMI or a screen at the production line via VGA or Ethernet port and display image data simultaneously, operators can view inspection results and find problems earlier.

64-bit computing:
As image analysis applications are required to manage large amounts of data, most mainstream software tools in this segment utilize 64-bit instructions. It is necessary, therefore, to deploy a vision system that supports 64-bit computing.

System storage:
System storage capacity can determine whether the vision system is able to run full PC OS and third- party APIs, in addition to the amount of image samples the system can store for matching and comparison.

TCO:
TCO is not determined solely by the nominal price tag of the system, but rather a combination of factors, including space usage, peripheral support, system expandability and software development costs.

Space usage:The physical size of the vision system, including external cabling, should be considered as production space cost. External wiring and cabling, as well as extended peripherals such as PWM light source controllers, must also be taken into account.

System expansion cost:The number of channels the vision system provides defines its expandability. Conventional smart cameras, though cheaper in single unit price, present the need for more system units to accomplish necessary expansion, such that actual system costs are much higher. New generation smart camera systems provide multiple channels and GigE ports to support additional slave cameras, negating the requirement to install additional system units and reducing average channel expansion costs.

Software development and versatility:As mentioned, a manufacturing facility is comprised of multiple operation stands, among which effective communication and integration determines actual factory efficiency. If existing software resources can easily migrate across systems, human resource and development costs in deployment are dramatically reduced.

Conclusion

For modern mass production process, the implementation of automated inspection is crucial in guaranteeing manufacturing quality and productivity, a primary requirement in enhancement of corporate competiveness.

Time and money are always key factors defining competiveness, and it is important for system implementers to choose a system that effectively minimizes cost and time-to-market.

 New smart cameras define a new category of vision system that singularly realizes high-performance, maximum integration, easy deployment, space efficiency and minimal total cost of ownership, well beyond what conventional systems can achieve.