Components of a Machine Vision System – Cameras, Lenses, Lighting & More

Every day, new supply chain and warehousing technologies deliver exciting possibilities: the chance for greater efficiency, improved safety, and exceptional quality control — and that’s only the beginning. Currently, many of the most impactful automated solutions are underscored by an important technology: machine vision.

Granting computerized systems the power of sight, machine vision (MV) boasts many applications within the modern warehouse and beyond. These solutions allow robotic systems to sense and perceive their surroundings in a human-like capacity and, if properly integrated, can play a powerful role in driving AI-centric automated solutions.

This cutting-edge technology produces powerful benefits spanning numerous industries. The advantages are particularly noteworthy in warehouses and distribution centers, with machine vision providing impressive tracking opportunities and quality control checks for items moving down high-speed conveyor lines. Many automated and semi-automated warehouses also use machine vision for pick-point verification or integrate MV systems with robotic picking solutions.

Machine vision systems are also widely used in manufacturing, with assembly verifications and surface inspections driving improvements in efficiency and quality control. These systems also have a role to play in logistics and transportation, delivering more effective asset tracking along with powerful surveillance opportunities.

There is no denying the value of machine vision in today’s automated environments, but a few caveats must be considered: these systems can be expensive to implement and somewhat difficult to integrate without the proper expertise. As such, it is important to understand what machine vision involves and when it promises the highest return on investment. We will dive into these details below, highlighting critical components and integration opportunities along the way.

Machine Vision Components

Like the human eyes they seek to emulate, machine vision systems are highly complex. They feature a variety of carefully-integrated components that allow them to obtain detailed visual input and to analyze it effectively. The most important components are highlighted below:

1. Machine Vision Cameras

Machine vision systems rely on specialized cameras to capture digital images. A variety of factors play into camera selection, with multiple types of image sensors used to produce high-quality visuals. Examples include:

CCD (Charge-Coupled Device). As complex semiconductor devices, CCDs are capable of transforming lights into electrical signals. These, in turn, make it easier to create digital images. Advantages of CCD include high-resolution images and exceptional sensitivity to light, although these are accompanied by a few noteworthy downsides: excessive power consumption and slower-than-expected readout speeds. What’s more, CCD solutions are often more expensive to implement.
CMOS (Complementary Metal-Oxide-Semiconductor). A common alternative to CCDs, CMOS solutions are appreciated for their limited power consumption and general cost-effective nature. Their functionality is similar: CMOS sensors allow light to turn into electrical signals and produce images. But CMOS takes an individual approach to reading out the signals, thereby resulting in far faster readout speeds.

MV cameras may also be classified based on how they capture images: in a single frame or one line at a time:

Area scan cameras. Sometimes referred to as two-dimensional MV cameras, area scan systems can use either CCD or CMOS sensors to capture both low and high-resolution images. These rely on a single-frame approach but are still highly versatile, with a variety of mounting options available. Under this approach, field of view and depth perception are sometimes limited, with details also lost in high-contrast areas.
Line scan cameras. Offering sequential solutions for capturing images, line scan cameras are preferred when high-resolution visuals are needed. Meanwhile, high line rates make it possible to capture excellent detailed images when objects are in motion. These are not quite as versatile as their area scan counterparts, however, and often more expensive as well.
Smart cameras. Capable of integrating camera, processing, and communication functions, smart cameras are often favored in applications where simplicity and compactness are key. While some models may not match the highest resolutions or specialized capabilities of area or line scan systems, ongoing advancements continue to improve their performance and versatility.

2. Camera Lenses

The camera lens plays an important role in determining the quality of images generated by MV systems. As such, lenses should be a priority when selecting cameras.

The main function of the lens is to focus light onto the sensor, thereby ensuring a clear image of the object to be captured. The lens determines how much light actually reaches the sensor and can also impact these essentials:

Focal length. As one of the most important lens specifications, the focal length forms the distance between the optical center of the lens and the precise location in which light rays meet to produce an image. This can have a huge impact on magnification and field of view.
Magnification. Functioning as a comparison of an object’s appearance (within an image) versus its real-world size, magnification depends on the size of the sensors and the focal length. Higher magnification can be useful when detailed images are required.
Field of view. The field of view (FOV) determines the extent to which an observable space can be viewed via the camera lens. The focal length plays a critical role in shaping FOV, with long focal lengths delivering a narrow FOV while shorter focal lengths are capable of producing a wide FOV.

MV systems typically rely on one of two main types of focal lenses: fixed or variable.

Fixed. Named for their consistent focal length, fixed lenses are unable to zoom but can be optimized for certain fields of view. Cost-effective and easy to set up, these lenses work well in compact locations.
Variable. Providing the opportunity to adjust the focal length, variable lenses tend to be more versatile, especially when differing fields of view are required. However, these are also more complex and often, more expensive than their fixed counterparts.

3. Lighting Techniques

Regardless of lens selection, proper lighting is important for producing strong contrast and enhancing image quality. This is an especially significant consideration when detailed images are required, as poor lighting can compromise the efficacy of otherwise high-quality cameras or lenses.

Valuable lighting strategies include:

Backlighting. Involving a bright light behind the object, backlighting provides one of the easiest solutions for improving contrast and especially for making the object’s edges (or possible defects) more visible.
Diffuse lighting. By scattering light, it is possible to create a softer approach to illumination. This limits shadowing, creating consistent images and ensuring that MV systems are capable of pinpointing defects.
Structured lighting. Patterns can be projected to purposefully create distortions, which actually provide valuable insight into the shape or surface of a given object. This is an increasingly prominent strategy for three-dimensional analysis.

4. Image Processing and Vision Software

While it is certainly important to capture high-quality images, processing also matters. This ensures that machine vision systems interpret what they ‘see’ correctly. Today’s most cutting-edge solutions deliver impressive processing capabilities:

Optical character recognition (OCR). It is possible for MV solutions to automatically recognize and even interpret text from barcodes or labels. This, in turn, makes it easier to track items as they move about the warehouse environment. OCR can also be useful for cross-docking purposes and is an increasingly utilized strategy for improving quality control.
Artificial intelligence. Today’s MV systems are increasingly AI-powered, as this allows for automated solutions such as object detection and pattern detection. This holds huge promise for moving beyond the rules-based machine vision strategies that once dominated the warehouse environment.
Deep learning. Drawing inspiration from neural networks, deep learning is a specific type of machine learning that echoes human processes for complex decision-making. Models such as convolutional neural networks (CNN) can be useful for image classification. This is obviously useful for sorting products but may also allow machine vision systems to more easily identify defects. Issues such as damaged packaging can also be identified via anomaly detection functionality.

5. Integration with Automation Systems

Machine vision cannot reach its full potential unless it is properly integrated with other warehousing or manufacturing systems, including increasingly automated solutions. Communication protocols play heavily into this, as they allow for the efficient exchange of information between cameras and other devices. Communication protocols promote interoperability between MV system components. Ethernet/IP protocols are especially common, but Input/Output (I/O) signal protocols promise to boost synchronization.

Programmable Logic Controllers (PLCs) are often used alongside MV to boost automation for vision-based initiatives. These can serve as central control units, communicating with essential components via I/O signals and Ethernet protocols.

Common Applications of Machine Vision Systems

MV systems are increasingly versatile and can be used for a variety of important warehousing, manufacturing, and supply chain functions. In general, these systems drive automation and in turn, dramatic improvements in efficiency. Specific examples include:

Pallet scanning. Pallet locations and contents can be verified through the use of advanced MV systems, which are capable of measuring pallet dimensions, defect detection, and \label inspection.
Pick and pack. Although already efficient, automated pick and pack systems can see dramatic improvements when MV solutions are used. This approach allows for picking optimization, with MV systems helping to identify the best strategies based on the size and location of various items.
Inventory management. Machine vision can be instrumental in the ongoing effort to prevent stockouts or overstocking. Upon capturing images of products as they are stored or in transit, MV can count these in real-time to keep inventory records up to date.

Emerging Technologies and Future Trends of Machine Vision

Advancements in artificial intelligence and machine learning promise to transform how machine vision is leveraged, especially in this era of data-driven warehousing. AI-based image recognition represents one of the most exciting innovations, with deep learning promising to further enhance sorting and defect detection. Moving forward, frame grabbers will also become more common, driving faster data transmission along with enhanced flexibility with machine vision integrations.

Leverage the Power of Machine Vision With Peak Technologies

From camera lenses to image processing and integration with enterprise resource planning (ERP) and warehouse management systems (WMS), machine vision systems call for a complex blend of components. These systems can be highly valuable in the modern warehouse or manufacturing environment, but their full potential cannot be reached unless they are implemented strategically. Therein lies the need for end-to-end machine vision integration solutions.

Our experts at Peak Technologies can help you determine which machine vision solutions are best for automating many parts of your operation. . We recognize the importance of strategic integration and are available to guide this process. Reach out today to learn more about our machine vision integration services.