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Exploring Industrial Cameras: A Guide for Engineers in Life Sciences, Semiconductors, and Automotive Fields 

In the bustling landscape of industrial camera offerings, discerning the parameters that genuinely define a camera’s worth can be a daunting task. This article serves as a compass, steering you through six fundamental properties that should illuminate your path when selecting an industrial camera. While the first three aspects play a pivotal role in aligning with your camera needs, the latter three hold significance if your requirements lean towards unique settings, external conditions, or challenging light environments.

    1. Resolution: unveiling the finer details. Imagine your camera as a painter’s canvas and resolution as the number of dots that bring your masterpiece to life. In simple terms, resolution is the number of pixels forming the image, determining its level of detail. For instance, a camera labeled 4096 x 3008 pixels amounts to a pixel symphony of around 12.3 million, or 12.3 megapixels. Yet don’t be swayed solely by megapixels. Focus on the pixel count on both the horizontal (X) and vertical (Y) axes. A 12-megapixel camera might sport configurations like 4000 x 3000 pixels, 5000 x 2400 pixels, or 3464 x 3464 pixels, each tailor-made for your observation intent and image format.
    1. Frame rate: capturing motion in real-time. The frame rate, akin to a movie’s frame sequence, dictates how swiftly your camera captures moving scenes. With figures like 46.5/74.0/135 denoting your camera’s capabilities, it reveals the number of images taken in different modes. Burst mode captures a rapid series of images, while Max. streaming ensures a consistent flow despite interface limitations. The elegance of Binning also plays a role, making it an adept solution for scenarios craving clarity in dim light and minimal noise.
    1. Connectivity: bridging the camera to your system. The camera’s connectivity interfaces, such as USB3 and GigE, shape its rapport within your system.

USB3 Interface: Like a speedy expressway for data, USB3 suits real-time applications like quality control and automation. Its straightforward nature adapts to diverse setups.

GigE Interface: This Ethernet-infused interface excels in robust, long-distance connections. Tailored for tasks like remote monitoring and industrial inspection, it basks in Ethernet’s reliability. Choosing the best fit: USB3 facilitates swift, direct communication, while GigE emerges triumphant in extended cable spans and networking. Your choice hinges on data velocity, distance, and infrastructure compatibility.

    1. Dynamic range: capturing radiance and shadow. Imagine your camera as an artist of light, skillfully capturing both dazzling radiance and somber shadows. Dynamic range defines this ability, representing the breadth of brightness levels the camera can encapsulate. Think of it as a harmony between light and dark. Technical folks may refer to it as the Ratio of Signal to Noise. It’s influenced by the camera’s design and the sensor’s performance. HDR mode is also worth noting, enhancing contrast by dividing the integration time into phases, each independently calibrated for optimal results.
    1. Sensitivity: shining in low-light environments. Your camera’s sensitivity determines its prowess in low-light scenarios. This sensitivity is akin to the ability to see in dimly lit spaces. Some cameras excel at this, providing a lifeline in settings with scarce illumination. Sensitivity’s secret lies in the art of collecting light while taming noise, finding the sweet spot between clear images and environmental challenges.
    1. Noise: orchestrating image purity. In the world of imagery, noise is akin to static in an audio recording—distracting and intrusive. Noise takes multiple forms and can mar image quality:

Read noise: This error appears when converting light to electrical signals. Faster speeds can amplify read noise, affecting image quality. Here, sensor design quality is a decisive factor.

Dark current noise: Under light exposure, sensors can warm up, introducing unwanted thermal electrons. Cooling methods can mitigate this thermal interference.

Patterns/artifacts: Sometimes, images bear unexpected patterns or shapes due to sensor design inconsistencies. Such artifacts disrupt accuracy, especially in low-light conditions. By understanding and adeptly managing these noise sources, CMOS industrial cameras have the potential to deliver superior image quality across diverse applications.

In the realm of industrial cameras, unraveling the threads of resolution, frame rate, connectivity, dynamic range, sensitivity, and noise paints a vivid portrait of informed decision-making. For engineers in life sciences, semiconductors, and automotive domains, this guide stands as a beacon, ushering them toward optimal camera choices that harmonize with their unique demands and aspirations.

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The Evolution of Barcode Scanning in Logistics Automation

 

Barcodes have played a pivotal role in revolutionizing supply chains since the 1970s. Traditional LED and laser scanners have been the go-to solution for reading barcodes, but with advancements in technology, new possibilities have emerged.

Here, I explore the limitations of traditional scanners and the rise of camera-based barcode scanners empowered by image analysis systems. I will delve into the intricate operations performed by these scanners and their superior efficiency in barcode location and decoding. Additionally, I will discuss the ongoing research in computer vision-based barcode reading techniques and the broader impact of machine vision in logistics beyond barcode scanning.

The limitations of traditional scanners

Traditional barcode readers operate by shining LED or laser light across a barcode, with the reflected beam detected by a photoelectric cell. While simple and effective in their time, these scanners have certain limitations that hinder their performance and restrict their application range. They require prior knowledge of barcode location, struggle with complex scenes, and are unable to read multiple barcodes simultaneously. Moreover, low-quality barcodes pose challenges, potentially leading to losses in time, money, and reputation.

The rise of camera-based barcode scanners

Camera-based barcode scanners, empowered by image analysis systems, have emerged as a game-changer in logistics automation. These scanners perform intricate operations, starting with image acquisition and preprocessing. Images are converted to grayscale, noise is reduced, and barcode edges are enhanced using various filters. Binarization is then applied, isolating black and white pixels for decoding. Unlike traditional scanners, image-based barcode scanners excel in barcode location and decoding. They eliminate the need for prior knowledge of barcode position and can locate and extract multiple barcodes in a single image.

The advantages of optical barcode scanners

As technology progresses, optical barcode scanners are gradually replacing LED and laser-based solutions, offering superior efficiency and performance. Computer vision-based barcode reading techniques have sparked extensive research, addressing challenges in both location and decoding steps. Barcode localization, the most intricate part, involves detecting and extracting barcodes accurately despite illumination variations, rotation, perspective distortion, or camera focus issues. Researchers continually refine barcode extraction techniques, using mathematical morphology and additional preprocessing steps for precise recognition.

Beyond barcode scanning: the impact of machine vision in logistic

The impact of machine vision in logistics extends beyond barcode scanning. Robot-operated warehouses, such as those employed by Amazon, rely on 2D barcodes to navigate shelves efficiently. Drones equipped with computer vision capabilities open new possibilities for delivery services, enabling autonomous and accurate package handling.

Machine vision technology is revolutionizing the way logistics operations are conducted, enhancing efficiency, accuracy, and overall customer experience.

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The Benefits of Mobile Handheld and Stationary Code Readers

Ensuring reliable traceability of products and assembly is critical in industries such as automotive, pharmaceuticals, and electronics. Code readers are essential in achieving this, with stationary and mobile handheld readers being the two most popular options. In what situations is it more appropriate to use one type over the other?

Stationary optical ID sensors

Stationary optical ID sensors offer simple and reliable code reading, making them an excellent option for ensuring traceability. They can read various codes, including barcodes, 2D codes, and DMC codes, and are permanently installed in the plant. Additionally, with their standardized automation and IT interfaces, the information readout can be passed on to the PLC or IT systems. Some variants also come with an IO-Link interface for extremely simple integration. The modern solution offers additional condition monitoring information, such as vibration, temperature, code quality, and operating time, making them a unique multi-talent within optical identification.

Portable code readers

Portable code readers provide maximum freedom of movement and can quickly and reliably read common 1D, 2D, and stacked barcodes on documents and directly on items. Various applications use them for controlling supply processes, production control, component tracking, quality control, and inventory. The wireless variants of handheld code readers with Bluetooth technology allow users to move around freely within a range of up to 100 meters around the base station. They also have a reliable read confirmation system via acoustic signal, LEDs, and a light spot projected onto the read code. Furthermore, the ergonomic design and highly visible laser marking frames ensure fatigue-free work.

Both stationary and mobile handheld barcode readers play an essential role in ensuring reliable traceability of products and assembly in various industries. Choosing the right type of barcode reader for your application is crucial to ensure optimal performance and efficiency. While stationary code readers are ideal for constant scanning in production lines, mobile handheld readers offer flexibility and reliability for various applications. Regardless of your choice, both devices offer simple operation and standardized automation and IT interfaces, making them essential tools for businesses that rely on efficient code reading.

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Using Vision Sensors to Conquer 1D and 2D Barcode Reading Applications

As many industries trend towards the adoption and use of two-dimensional barcodes and readers, the growth in popularity, acceptance of use, and positive track record of these 2D code readers offer a better way to track data. Vision-sensing code readers have many benefits, such as higher read rate performance, multi-directional code detection, simultaneous multiple codes reads, and more information storage.

While traditional red line laser scanners or cameras with decoding and positioning software are commonly used to read barcodes, there are three main types of barcodes: 1D, 2D, and QR codes. Each type has different attributes and ways of reading.

1D barcodes are the traditional ladder line barcodes typically seen in grocery stores and on merchandise and packaging. On the other hand, 2D Data Matrix codes are smaller than 1D barcodes but can hold quite a bit more information with built-in redundancy in case of scratches or defacement. QR codes, which were initially developed for the automotive industry, can hold even more information than Data Matrix codes, were initially developed for the automotive industry to track parts during vehicle manufacturing and are now widely used in business and advertising.

There are various types of vision sensors for reading different types of barcodes. QR codes are often used in business and advertising, while micro QR codes are typically seen in industrial applications such as camshafts, crankshafts, pistons, and circuit boards. Deciphering micro QR codes typically require an industrial sensor.

The need to easily track products and collect information about their whereabouts has been a long-standing problem in manufacturing and industrial automation. While one-dimensional barcodes have been the traditional solution, advances in one-dimensional code reading continue to improve. New hardware, code readers, and symbology, however, have made an emergence, and new image-based scanners are becoming a popular alternative for data capture solutions.

In summary, vision sensors are becoming increasingly important in 1D and 2D barcode reading applications due to their higher read rate performance, multi-directional code detection, simultaneous multiple codes read, and more information storage. As the need for tracking products and collecting information about their whereabouts continues to grow, industries will benefit from the use of vision sensors to improve efficiency and accuracy.

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Embedded vision – What It Is and How It Works

Embedded vision is a rapidly growing field that combines computer vision and embedded systems with cameras or other imaging sensors, enabling devices to interpret and understand the visual world around them – as humans do. This technology, with broad applications, is expected to revolutionize how we interact with technology and the world around us and will likely play a major role in the Internet of Things and Industry 4.0 revolution.

Embedded vision uses computer vision algorithms and techniques to process visual information on devices with limited computational resources, such as embedded systems or mobile devices. These systems use cameras or other imaging sensors to acquire visual data and perform tasks on that data, such as image or video processing, object detection, and image analysis.

Applications for embedded vision systems

Among the many applications that use embedded vision systems are:

    • Industrial automation and inspection
    • Medical and biomedical imaging
    • Surveillance and security systems
    • Robotics and drones
    • Automotive and transportation systems

Hardware and software for embedded vision systems

Embedded vision systems typically use a combination of software and hardware to perform their tasks. On the hardware side, embedded vision systems often use special-purpose processors, such as digital signal processors (DSPs) or field-programmable gate arrays (FPGAs), to perform the heavy lifting of image and video processing. On the software side, they typically use libraries or frameworks that provide pre-built functions for tasks, such as image filtering, object detection, and feature extraction. Some common software libraries and frameworks for embedded vision include OpenCV, MATLAB, Halcon, etc.

It’s also quite important to note that the field of embedded vision is active and fast moving with new architectures, chipsets, and software libraries appearing regularly to make this technology more available and accessible to a broader range of applications, devices, and users.

Embedded vision components

The main parts of embedded vision include:

    1. Processor platforms are typically specialized for handling the high computational demands of image and video processing. They may include digital signal processors (DSPs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs).
    2. Camera components refer to imaging sensors that acquire visual data. These sensors can include traditional digital cameras and specialized sensors such as stereo cameras, thermal cameras, etc.
    3. Accessories and carrier boards include the various additional hardware and components that interface the camera with the processor and other peripherals. Examples include memory cards, power supplies, and IO connectors.
    4. Housing and mechanics are the physical enclosures of the embedded vision system, including the mechanics that hold the camera, processor, and other components in place, and the housing that protects the system from external factors such as dust and water.
    5. The operating system runs on the processor. It could be a custom firmware or a general-purpose operating system, like Linux or Windows.
    6. Application SW is the software that runs on the embedded vision system to perform tasks such as image processing, object detection, and feature extraction. This software often uses a combination of high-level programming languages, such as C++, Python, and lower-level languages, like C.
    7. Feasibility studies evaluate a proposed solution’s technical and economic feasibility, identifying any risks or possible limitations that could arise during the development. They are conducted before the development of any embedded vision systems.
    8. Integration interfaces refer to the process of integrating the various components of the embedded vision system and interfacing it with other systems or devices. This can include integrating the camera, processor, and other hardware and developing software interfaces to enable communication between the embedded vision system and other systems.

Learn more here about selecting the most efficient and cost-effective vision product for your project or application.

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Understanding Image Processing Standards and Their Benefits

In the industrial image processing world, there are standards – GenICam, GigE Vision, and USB3 Vision – that are similar to the USB and Ethernet standards used in consumer products. What do these image processing standards mean, and what are their benefits?

The GenICam standard, which is maintained by the European Machine Vision Association (EMVA), serves as a base for all the image processing standards. This standard abstracts the user access to the features of a camera and defines the Standard Feature Naming Convention (SFNC) that all manufacturers use so that common feature names are used to describe the same functions.

Additionally, manufacturers can add specific “Quality of Implementation” features outside of the SFNC definitions to differentiate their products from ones made by other manufacturers. For example, a camera can offer specific features like frame average, flat field correction, logic gates, etc. GenICam/GigE Vision-based driver and software solutions from other manufacturers can also use these features without any problem.

“On-the-wire” standards

USB3 Vision and GigE Vision are “on-the-wire” interfaces between the driver and the camera. These standards are maintained by the Automated Imaging Association (AIA). You are probably familiar with “on-the-wire” standards and their advantages if you have used plug-and-play devices like USB memory sticks, USB mice, or USB hard disks. They work together without any problem, even if they are made by different manufacturers. It’s the same thing with GenICam/GigE Vision/USB3 Vision-based driver/software solutions. The standards define a transport layer, which controls the detection of a device, configuration (register access), data streaming (device detection), and event handling, and connects the interface to GenICam (Figure 1).

USB3 Vision builds on the GigE Vision standard by including accessories like cables. The mechanics are part of the standard and defines lockable cable interfaces, as one example. This creates a more robust interface for manufacturing environments.

Are standards a must-have?

Technically, standards aren’t necessary. But they make it possible to use products from multiple manufacturers and make devices more useful in the long term. For a historical comparison, look at USB 2.0 cameras and GigE Vision. USB 2.0 industrial cameras were introduced in 2004 and only worked with proprietary drivers (Figure 2) between the client and Vision Library/SDK and between the driver and camera. Two years later, Gigabit Ethernet cameras were introduced with the GigE Vision image processing standard, which didn’t require proprietary drivers to operate.

In the case of a system crash, users of the USB 2.0 cameras wouldn’t know whether the proprietary driver or the software library was to blame, which made them difficult to support. During the decision phase of selecting sensors and support, the customer had to keep the product portfolio in mind to meet their specifications. Afterward, the application was implemented and only worked with the proprietary interfaces of the manufacturer. In case of future projects or adaptions –for example, if a new sensor was required –it would have been necessary for the manufacturer to offer this sensor. Otherwise, it was necessary to change the manufacturer, which meant that a new implementation of the software was necessary as well. In contrast, flexibility is a big advantage with Gigabit Ethernet cameras and GigE Vision: GigE Vision-compliant cameras can be used interchangeably without regard to the manufacturer.

Despite this obvious benefit, USB cameras are more prevalent in certain image processing fields like medicine, given that the applications define the camera’s sensor resolution, image format and image frequency (bandwidth), and the environment for the purpose of cable length, frame grabber, or digital camera solution. With such tightly-defined requirements, USB cameras solve the challenges of these applications.

It’s hard to believe, but a few years ago, there weren’t any standards in the image processing market. Each manufacturer had its own solution. These times are gone – the whole market has pulled together, to the benefit of customers. Because of the standards, the interaction between hardware, driver, and software delivers the experience of a uniform piece. The quality of the market is improved. For the customer, it is easier to make product decisions since they are not locked into one company’s portfolio. With standards-compliant products, the customer can always choose the best components, independent of the company. With GenICam as a base, the image processing market offers the best interface for every application, either with GigE Vision or USB3 Vision.

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Picking Solutions: How Complex Must Your System Be?

Bin-picking, random picking, pick and place, pick and drop, palletization, depalletization—these are all part of the same project. You want a fully automated process that grabs the desired sample from one position and moves it somewhere else. Before you choose the right solution for your project, you should think about how the objects are arranged. There are three picking solutions: structured, semi-structured, and random.

As you can imagine, the basic differences between these solutions are in their complexity and their approach. The distribution and arrangement of the samples to be picked will set the requirements for a solution. Let’s have a look at the options:

Structured picking

From a technical point of view, this is the easiest type of picking application. Samples are well organized and very often in a single layer. Arranging the pieces in a highly organized way requires high-level preparation of the samples and more storage space to hold the pieces individually. Because the samples are in a single layer or are layered at a defined height, a traditional 2-dimensional camera is more than sufficient. There are even cases where the vision system isn’t necessary at all and can be replaced by a smart sensor or another type of sensor. Typical robot systems use SCARA or Delta models, which ensure maximum speed and a short cycle time.

Semi-structured picking

Greater flexibility in robotization is necessary since semi-structured bin picking requires some predictability in sample placement. A six-axis robot is used in most cases, and the demands on its grippers are more complex. However, it depends on the gripping requirements of the samples themselves. It is rarely sufficient to use a classic 2D area scan camera, and a 3D camera is required instead. Many picking applications also require a vision inspection step, which burdens the system and slows down the entire cycle time.

Random picking

Samples are randomly loaded in a carrier or pallet. On the one hand, this requires minimal preparation of samples for picking, but on the other hand, it significantly increases the demands on the process that will make a 3D vision system a requirement. You need to consider that there are very often collisions between selected samples. This is a factor not only when looking for the right gripper but also for the approach of the whole picking process.

Compared to structured picking, the cycle time is extended due to scanning evaluation, robot trajectory, and mounting accuracy. Some applications require the deployment of two picking stations to meet the required cycle time. It is often necessary to limit the gripping points used by the robot, which increases the demands on 3D image quality, grippers, and robot track guidance planning and can also require an intermediate step to place the same in the exact position needed for gripping.

In the end, the complexity of the picking solution is set primarily by the way the samples are arranged. The less structured their arrangement, the more complicated the system must be to meet the project’s demands. By considering how samples are organized before they are picked, as well as the picking process, you can design an overall process that meets your requirements the best.

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How to Choose the Best 4K Camera for Your Application

I need 4K resolution USB camera, what would you recommend me?

This is a common question that I am asked by customers, unfortunately the answer is not simple.

First, a quick review on the criteria to be a 4K camera. The term “4K” comes from TV terminology and is derived from full HD resolution.

Full HD is 1920 x 1080 = 2,073,600 total pixels
4K is 3840 x 2160 = 8,294,400 total pixels.

This assumes that the minimum camera resolution must be 8.3 Mpix. It is not guaranteed that the camera reaches 4K resolution, however, it is a basic recognition. For example, a camera with an IMX546 sensor has a resolution of 2856 x 2848 pixels. While the height of the sensor richly meets the conditions of 4K, the width does not. Even so, for our comparison I will use this camera because for certain types of projects (e.g. square object observation), it is more efficient than a 10.7 Mpix camera with a resolution 3856 x 2764 pixels.

Of course, 4K resolution isn’t the only parameter to consider when you are choosing a camera. Shutter type, frame rate and sensor size are also incredibly important and dictated by your application. And, of course, you must factor price into your decision.

Basic comparison

Sensor Mpixel Shutter Size Width Height Framerate Pricing
MT9J003 10.7 Rolling Shutter / Global Reset 1/2.35 3856 2764  

7.3

 

 $
IMX267 8.9 Global 1 4112 2176 31.9 $$
IMX255 8.9 Global 1 4112 2176 42.4 $$$
IMX226 12.4 Rolling Shutter / Global Reset 1/1.7

 

 4064 3044 30.7 $
IMX546 8.1 Global 2/3 2856 2848 46.7 $$$
IMX545 12.4 Global 1/1.1 4128 3008 30.6 $$$$

 

Shutter
Rolling shutter and global shutter are common shutter types in CMOS cameras. A rolling shutter sensor has simpler design and can offer smaller pixel size. It means that you can use lower cost lenses, but you must have in mind that you have limited usage with moving objects. A workaround for moving objects is a rolling shutter with global reset functionality which helps eliminating the image distortion.

Frames Per Second
The newest sensors offer a higher frame rate than the USB interface can handle. Check with the manufacturer; not everyone is able to get the listed framerate because of technical limitations caused by the camera.

Sensor Size
Very important information. Other qualitative information should also be considered, not only of the camera but also of the lens used.

Price
Global shutter image sensors are more expensive than rolling shutter ones. For this reason, the prices of global shutter cameras are higher than the rolling shutter cameras. It is also no secret that the image sensor is the most expensive component, so it is understandable that the customer very often bases the decision on the sensor requirements.

Advanced comparison

Sensor Pixel size EMVA report Dynamic range SNR Preprocessing features
MT9J003 1.67 link 56.0 37.2 *
IMX267 3.45 link 71.0 40.2 **
IMX255 3.45 link 71.1 40.2 ***
IMX226 1.85 link 69.2 40.3 **
IMX546 2.74 link 70.2 40.6 ****
IMX545 2.74 link 70.1 40.3 ****

 

There are many other advanced features you can also consider based on your project, external conditions, complexity of the scene and so on. These include:

Pixel Size
Sensor size from the basic comparison is in direct correlation with the size of the pixel because the size of the pixel multiplied by the width and height gives you the size of the sensor itself.

EMVA Report
EMVA 1288 is great document comparing individual sensors and cameras. In case you want the best possible image quality and functionality of the whole system, comparison is an important component in deciding which image sensor will be in your chosen camera. EMVA 1288 is the standard for measurement and presentation of specifications for machine vision sensors and cameras. This standard determines properties like signal-to-noise ratio, dark current, quantum efficiency, etc.

Dynamic Range
Dynamic range is one of the basic features and part of EMVA 1288 report as well. It is expressed in decibels (dB). Dynamic range is the ratio between the maximum output signal level and the noise floor at minimum signal amplification. Simply, dynamic range indicates the ability of a camera to reproduce the brightest and darkest portions of an image.

SNR
Signal-to-noise ratio (SNR) is a linear ratio between recorded signal and total root mean squared noise. SNR describes the data found in an image. It establishes an indication as to the signal quality found in the image indicating with what amount of precision machine vision algorithms will be able to detect objects in an image.

 

Preprocessing Features

Do you build high-end product? Is the speed important for you?
You need to rely on the camera/image sensor features. Every update of an image sensor comes with more and more built-in features. For example:

  • Dual trigger, where you set two different levels of exposure and gain and each can be triggered separately.
  • Self-trigger – you set 2 AOI, the first one triggers image and second detects difference in the AOI.
  • Short exposure modes – you can set as fast as 2us between shutters.

Machine vision components continue to be improved upon and new features are added regularly. So, when you are selecting a camera for your application, first determine what features are required to meet your application needs. Filter to only the cameras that can meet those needs and use their additional features to determine what more you can do.

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Machine Vision: 5 Simple Steps to Choose the Right Camera

The machine vision and industrial camera market is offering thousands of models with different resolutions, sizes, speeds, colors, interfaces, prices, etc. So, how do you choose? Let’s go through 5 simple steps which will ensure easy selection of the right camera for your application. 

1.  Defined task: color or monochrome camera  

2.  Amount of information: minimum of pixels per object details 

3.  Sensor resolution: formula for calculating the image sensor 

4.  Shutter technology: moving or static object 

5.  Interfaces and camera selector: lets pick the right model 

STEP 1 – Defined task  

It is always necessary to start with the size of the scanned object (X, Y), or you can determine the smallest possible value (d) that you want to distinguish with the camera.

For easier explanation, you can choose the option of solving the measurement task. However, the basic functionality can be used for any other applications.

In the task, the distance (D) between the centers of both holes is determined with the measurement accuracy (d). Using these values, we then determine the parameter for selecting the right image sensor and camera.

Example:
Distance (D) between 2 points with measuring accuracy (d) of 0.05 mm. Object size X = 48 mm (monochrome sensor, because color is not relevant here)

Note: Monochrome or color?
Color sensors use a Bayer color filter, which allows only one basic color to reach each pixel. The missing colors are determined using interpolation of the neighboring pixels. Monochrome sensors are twice as light sensitive as color sensors and lead to a sharper image by acquiring more details within the same number of pixels. For this reason, monochrome sensors are recommended if no color information is needed.

STEP 2 – Amount of information

Each type of application needs a different size of information to solve. This is differentiated by the minimum number of pixels. Lets again use monochrome options.

Minimum of pixels per object details

  • Object detail measuring / detection       3
  • Barcode line width                                           2
  • Datamatrix code module width                4
  • OCR character height                                    16

Example:
The measuring needs 3 pixels for the necessary accuracy (object detail size d). As necessary accuracy (d) which is 0.05 mm in this example, is imaged on 3 pixels.

Note:
Each characteristic or application type presupposes a minimum number of pixels. It avoids the loss of information through sampling blurs.

STEP 3 – Sensor resolution

We already defined the object size as well as resolution accuracy. As a next step, we are going to define resolution of the camera. It is simple formula to calculate the image sensor.

S = (N x O) / d = (min. number of pixels per object detail x object size) / object detail size

Object size (O) can be describe horizontally as well as vertically. Some of sensors are square and this problem is eliminated 😊

Example:
S = (3 x 48 mm) / 0.05 mm = 2880 pixels

We looked at the available image sensors and the closest is a model with resolution 3092 x 2080 => 6.4Mpixels image sensor.

Note:
Pay attention to the format of the sensor.

For a correct calculation, it is necessary to check the resolution, not only in the horizontal but also in the vertical axis.

 

STEP 4 – Shutter technology

Global shutter versus rolling shutter.

These technologies are standard in machine vision and you are able to find hundreds of cameras with both.

Rolling shutter: exposes the motive line-by-line. This procedure results in a time delay for each acquired line. Thus, moving objects are displayed blurrily in the resulting motive through the generated “object time offset” (compare to the image).

Pros:

    • More light sensitive
    • Less expensive
    • Smaller pixel size provides higher resolution with the same image format.

Cons:

    • Image distortion occurs on moving objects

Global shutter: used to get distortion-free images by exposing all pixels at the same time.

Pros:

    • Great for fast processes
    • Sharp images with no blur on moving objects.

Cons:

    • More expensive
    • Larger image format

Note:
The newest rolling shutter sensors have a feature called global reset mode, which starts the exposure of all rows simultaneously and the reset of each row is released simultaneously, also. However, the readout of the lines is equal to the readout of the rolling shutter: line by line.

This means the bottom lines of the sensor will be exposed to light longer! For this reason, this mode will only make sense, if there is no extraneous light and the flash duration is shorter or equal to the exposure time.

STEP 5 – Interfaces and camera selector

Final step is here:

You must consider the possible speed (bandwidth) as well as cable length of camera technology.

USB2
Small, handy and cost-effective, USB 2.0 industrial cameras have become integral parts in the area of medicine and microscopy. You can get a wide range of different variants, including with or without housings, as board-level or single-board, or with or without digital I/Os.

USB3/GigE Vision
Without standards every manufacturer does their own thing and many advantages customers learned to love with the GigE Vision standard would be lost. Like GigE Vision, USB3 Vision also defines:

    • a transport layer, which controls the detection of a device (Device Detection)
    • the configuration (Register Access)
    • the data streaming (Streaming Data)
    • the handling of events (Event Handling)
    • established interface to GenICam. GenICam abstracts the access to the camera features for the user. The features are standardized (name and behavior) by the standard feature naming convention (SFNC). Additionally, it is possible to create specific features in addition to the SFNC to differentiate from other vendors (quality of implementation). In contrast to GigE Vision, this time the mechanics (e.g. lockable cable connectors) are part of the standard which leads to a more robust interface.

I believe that these five points will help you choose the most suitable camera. Are you still unclear? Do not hesitate to contact us or contact me directly: I will be happy to consult your project, needs or any questions.

 

 

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Which 3D Vision Technology is Best for Your Application?

3D machine vision. This is such a magical combination of words. There are dozens of different solutions on the market, but they are typically not universal enough or they are so universal that they are not sufficient for your application. In this blog, I will introduce different approaches for 3D technology and review what principle that will be the best for future usage.

Bonus:  I created a poll asking professionals what 3D vision technology they believe is best and I’ve shared the results.

Triangulation

One of the most used technologies in the 3D camera world is triangulation, which provides simple distance measurement by angular calculation. The reflected light falls incident onto a receiving element at a certain angle depending on the distance. This standard method relies on a combination of the projector and camera. There are two basic variants of the projections — models with single-line structure and 2-dimensional geometric pattern.

A single projected line is used in applications where the object is moving under the camera. If you have a static object, then you can use multiple parallel lines that allow the evaluation of the complete scene/surface. This is done with a laser light shaped into a two-dimensional geometric pattern (“structured light”) typically using a diffractive optical element (DOE). The most common patterns are dot matrices, line grids, multiple parallel lines, and circles.

Structured light

Another common principle of 3D camera technology is the structured light technique. System contains at least one camera (it is most common to use two cameras) and a projector. The projector creates a narrow band of light (patterns of parallel stripes are widely used), which illuminate the captured object. Cameras from different angles observe the various curved lines from the projector.

Projecting also depends on the technology which is used to create the pattern. Currently, the three most widespread digital projection technologies are:

  • transmissive liquid crystal,
  • reflective liquid crystal on silicon (LCOS)
  • digital light processing (DLP)

Reflective and transparent surfaces create challenges.

Time of Flight (ToF)

For this principle, the camera contains a high-power LED which emits light that is reflected from the object and then returns to the image sensor. The distance from the camera to the object is calculated based on the time delay between transmitted and received light.

This is really simple principle which is used for 3D applications. The most common wavelength used is around 850nm. This is called near infrared range, which is invisible for human and eye safety.

This is an especially great use since the camera can standardly provide 2D as well as 3D picture in the same time.

An image sensor and LED emitter are used as an all-in-one product making it simple to integrate and easy to use. However, a negative point is that the maximum resolution is VGA (640 x 480) and  for Z resolution expect +/- 1cm. On the other hand, it is an inexpensive solution with modest dimensions.

Likely applications include:

  • mobile robotics
  • door controls
  • localization of the objects
  • mobile phones
  • gaming consoles (XBOX and Kinect camera) or industrial version Azure Kinect.

Stereo vision

The 3D camera by stereo vision is a quite common method that typically includes two area scan sensors (cameras). As with human vision, 3D information is obtained by comparing images taken from two locations.

The principle, sometimes called stereoscopic vision, captures the same scene from different angles. The depth information is then calculated from the image pixel disparities (difference in lateral position).

The matching process, finding the same information with the right and left cameras, is critical to data accuracy and density.

Likely applications include:

  • Navigation
  • Bin-picking
  • Depalletization
  • Robotic guidance
  • Autonomous Guiding Vehicles
  • Quality control and product classification

I asked my friends, colleagues, professionals, as well as competitors, on LinkedIn what is the best 3D technology and which technology will be used in the future. You can see the result here.

As you see, over 50% of the people believe that there is no one principle which can solve each task in 3D machine vision world. And maybe that’s why machine vision is such a beautiful technology. Many approaches, solutions and smart people can bring solutions from different perspectives and accesses.