Monday, 28 November 2022

IMPROVING VISION WITH SHORTWAVE INFRARED LINE-SCAN CAMERAS

 Makers of machine vision systems have long relied on visible light cameras when building systems to give their customers the ability to perform quality control in all sorts of production environments. Recently, another type of sensor, the short-wave infrared (SWIR) linescan camera, has improved in resolution and come down in price enough that system integrators have started to develop new inspection systems that take advantage of the unique advantages offered by such cameras.

LOOKING WITH NEW EYES

The SWIR part of the spectrum, which is generally considered to be wavelengths between 900 and 2500 nm, can pick out features that are not immediately obvious in visible light. For instance, it’s excellent at sorting fruits and vegetables and detecting foreign objects, such as packaging, mixed in with the food.

Say the customer is a produce distributor who needs to inspect frozen peas, to make sure there’s no debris among them. If there’s a small piece of plastic, similar in shape, size, and color to the peas, machine vision using visible light might not notice it. SWIR light, however, is strongly absorbed by water, so frozen peas with their high water content would be very dark in the image. A piece of plastic, which has little or no water content, would instead reflect the light and stand out sharply from the peas, and the sorting machine could use an air jet to blow it out of the pile (see Fig. 1).

In a very different example, the photovoltaic industry can also benefit from the properties of SWIR. Inspecting the silicon wafers that go into solar cell arrays is crucial, as defects within them can severely impair their efficiency at converting sunlight to electricity. Visible light, however, can only look at the surface of the wafers. But at SWIR wavelengths, the wafers are transparent. That makes it possible to find cracks inside them that wouldn’t be apparent in a normal visual inspection.

HYPERSPECTRAL IMAGING – BASED ON AREA SCAN CAMERAS

SWIR cameras can also play a role in hyperspectral imaging, which relies on multiple wavelengths of light in applications ranging from inspecting food to sorting different types of plastic waste to characterizing different materials. Hyperspectral imaging includes both visible and SWIR wavelengths, and sometimes, depending on the application, goes further into the infrared to include midwave and longwave infrared wavelengths as well.

Hyperspectral imaging systems add a dimension to the images they collect. Though they are used in line scan setup, they rely on area (two-dimensional) detectors. That’s because the scans are passed through a prism, diffracting the light, with different wavelengths falling on different parts of the detector to create a layered image. Individual wavelengths supply different information about the objects being imaged.

Often, hyperspectral systems are used in research to develop applications. Then, once the best wavelengths for detecting what’s sought have been determined, they’re replaced with simpler line scan systems that only look at a limited number of wavelengths, for example by using pulsed LEDs.

A NARROWER VIEW

The major difference between a line scan camera and an area camera is apparent in the name. A line scan camera consists of a single row of pixels that image a narrow line across the object being scanned, whereas an area camera captures a much larger area in each frame. Each pixel absorbs light from the object and converts it into a charge, and adjacent lines add up to an image of the whole object. In order to do that, either the scanner or the object must move so that different sections are within the sensor’s field of view.

That movement makes a line scan camera well suited to a production environment, where parts being inspected often move along a conveyor belt, or where objects being sorted drop into a bin. Fruits and vegetables, for instance, typically fall past the detector. Because these applications already involve motion, they fit naturally with line scan cameras, while avoiding the risk of blurring the image that the motion might produce in an area camera. A line scan image is also less likely than an area image to contain faulty pixels that can hide the defects being sought, and they provide good resolution at an affordable price.

HOW TO SELECT AN INFRARED LINE SCAN CAMERA

THE WAVELENGTH

In deciding whether to turn to SWIR imaging for your application, it is important to know whether there is some aspect of the objects being viewed that stands out at SWIR wavelengths. In applications such as inspection of markings—labels and bar codes, for instance— there is no reason to use IR light, as visible imaging does a much better job, at a much lower cost.

Knowing the SWIR wavelength you need is often critical to the success of the setup. Which wavelength is best depends on the intended application. For food sorting based on moisture content, the typical wavelength used is 1450 nm, which is very strongly absorbed by water (see Fig. 2). Other food inspection applications might require different wavelengths. SWIR can be used to identify different aspects of food, such as the fat content in meat or a bruise in an apple, that reflect or absorb light differently from the area around them. It can measure how fresh a fish is. Or it can look for impurities.

Melamine, an industrial chemical that has been found to be contaminating powdered milk, stands out in SWIR light, for instance.

Nowadays, the typical SWIR camera uses a detector built from indium-gallium-arsenide, which is sensitive to light from 900 to 1700 nm. Some applications, however, require wavelengths as long as 2000 or 2500 nm, which is sometimes referred to as extended SWIR. The mining industry, for instance, sometimes turns to those wavelengths, which require a different or modified material in the detector.

No one specific wavelength is used for silicon wafer inspection. The silicon is transparent at wavelengths above 1200 nm, so anything beyond that works. Of course, finding small defects requires high resolution and often large magnification, and the shorter the wavelength is, the higher the resolution and the smaller the defect it can detect.

RESOLUTION

If the user wants to build a megapixel image from the camera— that is, an image with a million pixels—he can achieve that by scanning 1,000 lines that each have 1,024 pixels. So, a system that scans at a relatively low line rate of 1 kHz—1,000 lines per second—can capture a megapixel in one second. In order to achieve that without significant overlaps or gaps between the lines, the speed of the conveyor belt has to match with the line rate and the camera’s field of view.

The resolution of the system should also match the application, and system designers can figure that out by considering the field of view of their scanners and the size of the particle or defect they’re trying to find. Generally, people looking for foreign matter in fruits and vegetables are interested in macroscopic objects, so it’s easy to achieve the necessary resolution. Depending on the setup, the field of view may be up to one meter. So, a 512-pixel camera may be sufficient.

For silicon wafer inspection, the defects being sought are smaller, so the resolution needs to be higher (see Fig. 3). Such a system might call for a 2,048-pixel camera. One relatively new technique for finding smaller defects in wafers is an approach called “transflection,” a combination of transmission and reflection. Light that has transmitted for a short distance inside the wafer is then reflected, and any crack in the way will cast a shadow, which tends to be bigger than the crack itself and therefore easier to spot. Transflection systems require careful optimization by the system designer.

NOISE

Noise is also important in these systems. The main contributor is the detector’s read noise and it defines the limits of detection. As the line rate increases, the exposure time, and thus the number of photons reaching the detector during the exposure time, decreases. At a rate of 1,000 lines per second, for instance, the exposure time can be a maximum of 1 ms. Increasing the line rate shortens the exposure time even more, so it becomes crucial that the noise doesn’t overwhelm the light signal collected by the detector. Of course, it is possible to increase the light intensity in the system so that more photons per second reach the detector, but that’s not always easy; it drives the cost of the system up and can create problems with heat. For this reason, detectors with low noise levels are desirable.

BEYOND CAMERAS

An inspection system includes several components that work in conjunction with the SWIR line scan camera. One key component is the light source, which can consist of LEDs, lasers, or halogen lamps. Of course, the source has to provide the right wavelength for the application, but there are other considerations as well. For instance, in order to get uniform illumination along the whole line of pixels, it might be best to match the camera with a line of LEDs. And the setup might be arranged in a number of different ways. For food sorting, perhaps the best place to locate the source would be next to the camera, while for semiconductor inspection it might make more sense to put the source on one side of the wafer and the camera on the other. Users might also want to look at a silicon wafer at an angle to see reflected light inside it, the better to notice small defects.

Another consideration is synchronizing the camera with the light source. As the objects move past the camera, the camera snaps individual frames. It’s important that the light be illuminating the objects at the moment the camera fires. The standard method is to send a triggering pulse to the camera and the light source at the same time, using an external trigger connector or something like the CameraLink data and control interface. That functionality is standard on any commercial machine vision camera. In some applications, users might look for different characteristics with two or three wavelengths. Triggering different sources, for example LEDs with different wavelengths, at different times allows the system to sort the wavelengths temporally.

AN EVOLVING FIELD

While machine vision systems based on visible light have been widely used for years, SWIR line scan imaging is relatively new and not as widespread. In part, that is due to the lower resolution of the IR systems, with maximum pixel counts of 2,048 as compared to 16,000 for visible.

But in recent years, the noise and resolution of SWIR cameras have improved at the same time the cost has come down. That has led to their adoption in high-volume applications such as food sorting and semiconductor inspection. Making higher resolution cameras will require building smaller pixels, and there are technological challenges that must be overcome to achieve that. The main focus of camera manufacturers today is in reducing the noise in the system and increasing the scanning speed.

THE RIGHT ANSWER

System integrators who build machine vision setups for their customers can benefit by using SWIR line scan cameras in their systems. For the right application, IR light provides the ability to see things that visible light does not. It can peer beneath the surface of a silicon wafer to look for cracks, for instance, or make inedible material stand out sharply from food items that in visible light may look very similar.

Machine vision integrators have several factors to consider when incorporating a SWIR line scan camera into their systems. Which wavelength is right for the application? Which light source works best? What level of resolution is necessary? Does the scanning speed match the speed at which the inspected objects are moving? Is the noise low enough to get a readable signal with that light source and scanning speed?

If these questions have the right answers, system integrators may find that SWIR line scan cameras provide the capabilities to give their customers the machine vision that they’re looking for.

TO KNOW MORE ABOUT LINE SCAN CAMERAS IN SINGAPORE CONTACT MVASIA INFOMATRIX PTE LTD AT +65 6329-6431 OR EMAIL US AT INFO@MVASIAONLINE.COM

3 WAYS TO LEVERAGE 3D TECHNOLOGY IN LOGISTICS

 In logistics, the demand for automation is rising due to growth in e-commerce and changing customer expectations. For companies to keep up with these conditions, they need to incorporate the latest innovations that will help them deliver to customers faster.


This is where 3D technology comes into play. 3D machine vision, such as Cognex’s 3D-A1000, is a camera system that captures and processes data from all three dimensions.

Within logistics, 3D can not only automate processes such as dimensioning, item detection, and presence/absence, but it can also increase throughput, improve process efficiency, and ensure order accuracy. In addition, unlike 2D, 3D technology is adaptable to ever-changing packaging, graphics, and artwork.

Here are 3 innovative ways to leverage 3D technology in logistics.

1. SIDE-BY-SIDE DETECTION:

This feature of 3D technology detects when items are unintentionally placed next to each other or on top of each other, and accidentally counted as one item instead of two. This can cause a customer to receive multiple items or no order at all, which can be extremely costly. Detecting side-by-side situations is important for ensuring no inventory is lost, preventing mis-sorts and incorrect or delayed shipments, and reducing manual rework. This type of detection can also correctly identify multi-packs such as a pack of soda bottles or a split-top box containing multiple items.

2.TOTE FILL:

This application tracks and measures tote fill height and volume, which allows customers to maximize the usage of their totes, ensure no products or equipment is damaged, and verify the contents of totes. Totes are useful for order fulfillment, to move goods around facilities, or for storage. However, many logistics companies need to automate the use of totes to ensure they’re used properly, which is why they rely on 3D technology.

3.LABEL PLACEMENT VERIFICATION:

Ensuring that labels are properly placed on packages is crucial to accurate order fulfillment. 3D technology can measure where a label or marking is placed on a package and detect where the label is overhanging. Accurate label detection and verification allows logistics companies to ship readable labels, avoid production line delays, and reduce manual work.

FOR MORE INFORMATION ON 3D MACHINE VISION SYSTEMS VISIT MVASIAONLINE.COM OR CONTACT US AT MVASIA INFOMATRIX PTE LTD +65 6329-6431 OR EMAIL US AT INFO@MVASIAONLINE.COM

Friday, 11 November 2022

HOW WILL THE LATEST IMAGE SENSORS IMPACT THE FUTURE OF MACHINE VISION?

 

WHAT IS AN IMAGE SENSOR?

An image sensor is a device that allows the camera to convert photons (light) into electrical signals. They are composed of millions of pixels on a single chip. The image sensor measures light intensity – but light's angle, spectrum, and other characteristics are also extracted.

For simple applications like photography, the intensity information of three-color bands (RGB) is adequate. However, for advanced sensing applications, such as autonomous vehicles, biomedical imaging, and robotics, extracting more information from the incident light could help machines to make better decisions.

SIZE MATTERS

The bigger, the better when it comes to image sensors. Because the sensor is the part of the camera capturing the image, it is critical to the quality of the resulting image. Sensor size and megapixel count are very closely connected. A larger camera sensor will give you better image quality because it gathers more light and delivers a higher megapixel count than a smaller sensor.

IMPROVING IMAGE SENSORS FOR MACHINE VISION

In the future, it is predicted that more cameras will be built for machines than people. This will be further accelerated by the rapid progress in machine learning and artificial intelligence. In addition, it is predicted that machine vision applications will substantially benefit from the multimodal measurement of light fields by advanced imaging sensors.

Some of the latest advances in image sensing have significantly impacted 3D imaging, event-based sensing, and nonvisible image sensing. According to innovations-report.com , the latest developments could enable autonomous vehicles to see around corners instead of just a straight line, biomedical imaging to detect abnormalities at different tissue depths, and telescopes to see through interstellar dust.

Optics play a major role in the performance of any imaging system. For optimal performance, it is critical to choose a lens that can accommodate the latest sensor technology. Computar's MPT 1.4" 45 Megapixel Series is engineered to optimize the capabilities of the latest industrial CMOS sensors.


TO KNOW MORE ABOUT MACHINE VISION DEALERS IN SINGAPORE CONTACT MVASIA INFOMATRIX PTE LTD AT +65 6329-6431 OR EMAIL US AT INFO@MVASIAONLINE.COM


Monday, 3 October 2022

WHAT IS MACHINE VISION?

 Machine vision is the technology used to provide imaging-based automatic inspection and analysis for such applications as process control, robot guidance, factory automation, and mechanical inspection, usually in industry.


According to Forbes , a machine vision system is a combination of software and hardware that usually incorporates:

  • * Sensors

  • * Frame-grabber

  • * Cameras (digital or analog)

  • * Sufficient lighting for cameras to capture quality images

  • * Software and computer capable of analyzing images

  • * Algorithms that can identify patterns necessary in some use cases

  • * Output such as a screen or mechanical components


HOW IS MACHINE VISION USED?

Machine vision is primarily used in industry for quality control to identify businesses production line mistakes, inspection, guidance, and more. Machine vision is valuable for factory automation in finding and correcting production line errors where they start before, they affect too many more products.

Machine vision is also helpful for manufacturing and warehouses, where they can expedite inventory control by reading barcodes and labels on various products and parts. Machine vision lenses are also used for finding a specific part and ensuring proper placement or positioning, so the production process runs as smoothly as possible. It is also used for machine vision gauging, where a fixed-mount camera distinguishes two or more points on an object as it goes through the production line to find discrepancies between the distances measured and thus finds production mistakes.

In agriculture, farms find machine vision beneficial when installed in farming equipment to monitor crops and detect their diseases. SWIR imaging is one application of machine vision that can be used in the agriculture and farming industry for produce inspection because its ability to see past the human eye.

The printing industry finds machine vision useful for catching printing defects for labels, packaging, and other print.

In healthcare and life sciences, machine vision lenses —such as these SWIR lenses —are used for microscopes, robotics, and medical machines, such as the well-known CT scanner.


WHY IS THE LENS A CRITICAL COMPONENT IN MACHINE VISION?

The data flows from the lens first. That makes the lens choice one of the most impactful decisions in determining how a machine vision system will perform. Computar's award-winning 45-megapixel machine vision MPT lens series ' floating design is ideal to deliver high-performance and high-level aberration correction at any working distance. In addition, the centering/alignment technology has astonishing performance from the image center to the corner, delivering the precise detail required for optimal machine vision performance.

Machine vision systems and their applications are constantly evolving. With continuous advancements in technology, robotics, and AI, machine vision will become a standard for improving quality, efficiency, and operations.


TO KNOW MORE ABOUT INDUSTRIAL MACHINE VISION CAMERAS DEALER IN SINGAPORE CONTACT MVASIA INFOMATRIX PTE LTD AT +65 6329-6431 OR EMAIL US AT INFO@MVASIAONLINE.COM



Monday, 29 August 2022

WHAT’S THE DIFFERENCE BETWEEN VISIBLE AND SWIR LENSES?

 Short-wave Infrared (SWIR) lenses are designed to operate in the 0.9-1.7 ┬Ám wavelength region. SWIR is close to visible light in that photons are reflected or absorbed by an object, providing the strong contrast needed for high-resolution imaging. SWIR is great for the Machine Vision and the Health & Sciences Industries because water vapor, fog, and certain materials such as silicon are transparent. SWIR imaging is also helpful because similar-looking colors visible to the human eye are easily differentiated using SWIR lenses.


HOW DOES IT WORK?

SWIR lenses are like visible cameras in the way they detect reflected light. Photons in the SWIR wavelength are reflected or absorbed by objects, allowing for high-resolution imaging with a strong contrast. This kind of technology is the only wavelength technology that can pierce through cloud coverage and capture a well-defined image.

For our ViSWIR series, according to Mr. Katsuya Hirano, Chief Optical Designer, CBC Group, for fully-corrected focus shift in visible and SWIR range (400nm-1,700nm): “By using ultra-low dispersion glass and low partial dispersion glass paired with superior design technology developed from Computar’s extensive optics experience, the focus shift is minimized within a few micron mm at a super wide range of wavelengths. With this, spectral imaging is achievable with a single sensor camera by simply syncing the lighting.”

With Computar's ViSWIR HYPER-APO lens series, it is unnecessary to adjust focus for differences. By adopting an APO floating design*, the focus shift is reduced at any wavelength and any working distance. This function makes SWIR lenses ideal for multiple applications, including machine vision, UAV, and remote sensing.


WHICH LENS IS THE BEST FOR MY INDUSTRY?

For the Machine Vision Industry as well as the Life Sciences Industry, we recommend our ViSWIR Series. These lenses achieve a clear and precise image visible to the SWIR range by applying a multilayer coating to absorb the specific light. A higher-resolution lens gives you greater specificity in designing and implementing the most efficient vision solutions. So, for medical devices and robotics, this is great for detail work and other short-range imaging.

For the Intelligent Transport Systems Industry and Government and Defense, a blend of visible and SWIR would be most helpful—visible imaging for distance and SWIR for detailed imaging.

Some lenses, such as ours, are designed to perform well with for both visible and SWIR, enabling cost-effective and performance imaging systems for a range of applications.

TO KNOW MORE ABOUT MACHINE VISION LENS DISTRIBUTORS IN MIDDLE EAST SINGAPORE CONTACT MVASIA INFOMATRIX PTE LTD AT +65 6329-6431 OR EMAIL US AT INFO@MVASIAONLINE.COM

Friday, 8 July 2022

SMART VS. STANDARD MACHINE VISION LENSES

 Machine vision lenses have exploded in popularity recently with the increasing demand for automation and robotics across various industries. With this explosion comes new imaging technology and a variety of machine vision lenses. Here we explore the differences between smart and standard machine vision lenses.


Smart lenses feature a P-Iris allow remote adjustment to improve contrast, clarity, resolution, and depth of field. With software configured to optimize performance, the P-Iris automatically provides the best iris position for optimal image quality in all lighting conditions. The auto-iris has its limitations. Selecting a precise iris value is not repeatedly attainable with the auto-iris lens. In addition, problems may occur because of the iris straying from its selected aperture value over time. Auto-iris lenses mainly adjust the level of light that would reach the sensor and are only reliable when the iris is fully opened or set to its smallest aperture. Therefore, it is challenging for auto-iris lenses to attain accurate mid-range values, which can result in image diffraction and aberrations.

Lens focusing capabilities vary as well. For example, the floating focus design of an intelligent lens delivers ultra-high resolution from near to far, and stepper motors enable precise focus control and high repeatability. A standard manual or autofocus lens can produce great results, but neither can be adjusted on the fly. They can lose focus with applications requiring the inspection of objects at various heights. Things outside the depth of field become out of focus and limit the vision application.

The convenience and time-saving factors of making remote adjustments can be the deciding factor in the type of lens chosen. Smart plug-and-play machine vision lenses are easy to install, control, and adjust remotely. After being plugged in via USB and installed, smart lenses can be fine-tuned using software installed on a Windows or Linux system.

More and more industries and applications are using machine vision each year. Smart lenses are advantageous for multiple machine vision applications, including automation, robotics, inspection, medical labs, manufacturing, warehouses, and just about any environment where image clarity is required. With this growth in popularity comes the demand for a more precise, efficient, and intelligent lens.

TO KNOW MORE ABOUT MACHINE VISION LENSES DEALERS SINGAPORE CONTACT MVASIA INFOMATRIX PTE LTD AT +65 6329-6431 OR EMAIL US AT INFO@MVASIAONLINE.COM

Friday, 24 June 2022

HOW DEEP LEARNING AUTOMATES PACKAGING SOLUTION INSPECTIONS

 Increasingly, packaging products require their own custom inspection systems to perfect quality, eliminate false rejects, improve throughput, and eliminate the risk of a recall. Some of the foundational machine vision applications along a packaging line include verifying that a label on a package is present, correct, straight, and readable. Other simple packaging inspections involve presence, position, quality (no flags, tears, or bubbles), and readability (barcode and date/lot codes present and scannable) on a label.


But packaging like bottles, cans, cases, and boxes—present in many industries, including food and beverage, consumer products, and logistics—can’t always be accurately inspected by traditional machine vision. For applications which present variable, unpredictable defects on confusing surfaces such as those that are highly patterned or suffer from specular glare, manufacturers have typically relied on the flexibility and judgment-based decision-making of human inspectors. Yet human inspectors have some very large tradeoffs for the modern consumer packaged goods industry: they aren’t necessarily scalable.

For applications which resist automation yet demand high quality and throughput, deep learning technology is a flexible tool that application engineers can have confidence in as their packaging needs grow and change. Deep learning technology can handle all different types of packaging surfaces, including paper, glass, plastics, and ceramics, as well as their labels. Be it a specific defect on a printed label or the cutting zone for a piece of packaging, Cognex Deep Learning can identify all of these regions of interest simply by learning the varying appearance of the targeted zone. Using an array of tools, Cognex Deep Learning can then locate and count complex objects or features, detect anomalies, and classify said objects or even entire scenes. And last but not least, it can recognize and verify alphanumeric characters using a pre-trained font library.


Here, we’ll explore how Cognex Deep Learning does all of the above for packagers and manufacturers.


PACKAGING DEFECT DETECTION


Machine vision is invaluable to packaging inspections on bottles and cans. In fact, in most factories, it is machine vision which not only inspects the placement of labels and wrapping but also places and aligns them during manufacturing.

Labeling defects are well-handled by traditional machine vision, which can capably detect wrinkles, rips, tears, warpage, bubbles, and printing errors. High-contrast imaging and surface extraction technology can capture defects, even when they occur on curved surfaces and under poor lighting conditions. Yet the metal surface of a typical aluminum can might confuse traditional machine vision with its glare as well as the unpredictable, variable nature of its defects, not all of which need to be rejected. Add to those challenging surface inspections countless forms and types of defects—for example, long scratches and shallow dents — and it quickly becomes untenable to explicitly search for all types of potential defects.

Using a novel deep learning-based approach, it’s possible to precisely and repetitively inspect all sorts of challenging metal packaging surfaces. With Cognex Deep Learning, rather than explicitly program an inspection, the deep learning algorithm trains itself on a set of known “good” samples to create its reference models. Once this training phase is complete, the inspection is ready to start. Cognex Deep Learning can identify and report all defective areas on the can’s surface which deviate outside the range of a normal acceptable appearance.


PACKAGING OPTICAL CHARACTER RECOGNITION


Hiding somewhere on almost all consumable packages, regardless of material or type, lies a date/lot code. Having these codes printed cleanly and readable is important not only for end-users and consumers doing their shopping but also for manufacturers during the verification stage. A misprinted, smeared, or deformed date/lot code printed onto a label on a bottle or package of cookies, for example, causes problems for both.

Typically, traditional machine vision could easily recognize and/or verify that codes are readable and correct before they leave the facility, but certain challenging surfaces make this too difficult. In these cases, a smeared or slanted code printed on specular material like a metal soda case could be read with some effort by a human inspector but not with much reliability by a machine vision inspection system. In these cases, packagers need an inspection system that can judge readability by human standards but, critically, with the speed and robustness of a computerized system. Enter, deep learning.

Cognex's deep learning OCR tool is able to detect and read the plain text in date/lot codes, verifying that their chains of numbers and letters are correct even when they are badly deformed, skewed, or—in the case of metal surfaces—poorly etched. The tool minimizes training because it leverages a pre-trained font library. This means that Cognex Deep Learning can read most alphanumeric text out-of-the-box, without programming. Training is limited to specific application requirements to recognize surface details or retrain on missed characters. All of these advantages help ease and speed implementation and contribute to successful OCR and OCV application results without the involvement of a vision expert.


PACKAGING ASSEMBLY VERIFICATION


Visually dependent assembly verification can be challenging for multi-pack goods which may have purposeful variation, as in the case of holiday-themed or seasonal offerings. These packs showcase different items and configurations in the same case or box.

For these sorts of inspections, manufacturers need highly flexible inspection systems which can locate and verify that individual items are present and correct, arranged in the proper configuration, and match their external packaging. To do this, the inspection system needs to be able to locate and segment several regions of interest within a single image, possibly in multiple configurations that can be inspected line-by-line to account for variations in packaging.

To locate individual items by their unique and varying identifiable characteristics, a deep learning-based system is ideal because it generalize each item’s distinguishable characteristics based on size, shape, color, and surface features. The Cognex Deep Learning software can be trained quickly to build an entire database of items. Then, the inspection can proceed by region, whether by quadrant or line-by-line, to verify that the package has been assembled correctly.


PACKAGING CLASSIFICATION


Kitting inspections require multiple capabilities of its automated inspection system. Consumer product multi-packs need to be inspected for the right number and type of inclusions before being shipped. Counting and identification are well-loved strengths of traditional machine vision. But to ensure that the right items are included in a multi-part unit requires classifying included products by category—for example, does a sunblock multi-pack contain two types of sunblock, or does it contain an extra sunblock lip balm?

This categorization is important yet remains out of reach for traditional machine vision. Luckily, Cognex's deep learning classification tool can easily be combined with traditional location and counting machine vision tools, or with deep learning-based location and counting tools if the kitting inspection deals with variable product types and requires artificial intelligence to distinguish the generalizing features of these types.

Deep learning-based classification works by separating different classes based on a collection of labelled images and identifies products based on these packaging discrepancies. If any of the classes are trained as containing anomalies, then the system can learn to classify them as acceptable or unacceptable.

New deep learning-enabled vision systems differ from traditional machine vision because they are essentially self-learning and trained on labeled sample images without explicit application development. These systems can also be trained on new images for new inspections at any time, which makes it a valuable long-term asset for growing businesses.

Deep learning-based software is also quick to deploy and uses human-like intelligence which is able to appreciate nuances like deviation and variation and outperform even the best quality inspectors at making reliably correct judgments. Most importantly, however, is that it is able to solve more complex, previously un-programmable automation challenges.

Manufacturers in the packaging industry are increasingly demanding faster, more powerful machine vision systems, and for good reason: they are expected to make a great number of products at a higher quality threshold and for less cost. Cognex is meeting customers’ rigorous requirements head-on by offering automated inspection systems that marry the power of machine vision with deep learning in order to manufacture packaging more cost effectively and robustly.

TO KNOW MORE ABOUT MACHINE VISION DEALER SINGAPORE FOR PACKAGING SOLUTIONS CONTACT MVASIA INFOMATRIX PTE LTD AT +65 6329-6431 OR EMAIL US AT INFO@MVASIAONLINE.COM