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.

When to use optical filtering in a machine vision application

Industrial image processing is essentially a requirement in modern manufacturing. Vision solutions can deliver visual quality control, identification and positioning. While vision systems have gotten easier to install and use, there isn’t a one-size-fits-all solution. Knowing how and when you should use optical filtering in a machine vision application is a vital part of making sure your system delivers everything you need.

So when should you use optical filtering in your machine vision applications? ALWAYS. Image filtering increases contrast, usable resolution, image quality and most importantly, it dramatically reduces ambient light interference, which is the number one reason a machine vision application doesn’t work as expected.

Different applications require different types of filtering. I’ve highlighted the most common.

Bandpass Filtering

Different light spectrums will enhance or de-emphasize certain aspects of the target you are inspecting. Therefore, the first thing you want to do is select the proper color/wavelength that will give you the best contrast for your application. For example, if you are using a red area light that transmits at 617nm (Figure 1), you will want to select a filter (Figure 3) to attach to the lens (Figure 2) that passes the frequency of the area light and filters out the rest of the color spectrum. This filter technique is called Bandpass filtering reference (Figure 4).

This allows only the light from the area light to pass through while all other light is filtered out. To further illustrate the kinds of effects that can be emphasized or de-emphasized we can look at the following images of the same product but with different filters.

Another example of Bandpass filtering can be seen in (Figure 9), which demonstrates the benefit of using a filter in an application to read the LOT code and best before sell date. A blue LED light source and a blue Bandpass filter make the information readable, whereas without the filter it isn’t.

Figure 9

Narrow Bandpass Filtering

Narrow bandpass filtering, shown in (Figure 10), is mostly used for laser line dimensional measurement applications, referenced in (Figure 11). This technique creates more ambient light immunity than normal Bandpass filtering. It also decreases the bandwidth of the image and creates a kind of black on white effect which is the desired outcome you want for this application.

Shortpass Filtering

Another optical filtering technique is shortpass filtering, shown in (Figure 12), which is commonly used in color camera imaging because it filters out UV and IR light sources to give you a true color image.

Figure 12

Longpass Filtering

Longpass filtering, referenced in (Figure 13), is often used in IR applications where you want to suppress the visible light spectrum.

Figure 13

Neutral Density Filtering

Neutral density filtering is regularly used in LED inspection. Without filtering, light coming from the LEDs completely saturates the image making it difficult, if not impossible, to do a proper inspection. Deploying neutral density filtering acts like sunglasses for your camera. In short, it reduces the amount of full spectrum light the camera sees.

Polarization Filtering

Polarization filtering is best to use when you have surfaces that are highly reflective or shiny. Polarization filtering can be deployed to reduce glare on your target. You can clearly see the benefits of this in (Figure 14).

Figure 14