Most of us think about vision with a human bias, since most of us are normally sighted with color stereo vision. We perceive distance, hues, shading, and intensity, for materials that emit or reflect light in the wavelengths 380 – 750 nm. Many machine vision problems can also be solved using monochrome or color light and sensors in the visible spectrum.
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Many applications are best solved or even only solved, in wavelengths that we cannot see with our own eyes. There are sensors that react to wavelengths in these other parts of the spectrum. Particularly interesting are short wave infrared (SWIR) and ultraviolet (UV). In this blog we focus on SWIR, with wavelengths in the range 0.9 – 1.7um.
Food processing and agricultural applications possible with SWIR. Consider the above images, where the visible image shows what appears to be a ripe apple in good condition. With SWIR imaging, a significant bruise is visible – as SWIR detects higher densities of water which render as black or dark grey. Supplier yields determine profits, losses, and reputations. Apple suppliers benefit by automated sorting of apples that will travel to grocery shelves vs. lightly bruised fruit that can be profitably juiced or sauced.
Whether controlling the filling apparatus or quality controlling the nominally filled bottles, SWIR light and sensors can see through glass or opaque plastic bottles and render fluids dark while air renders white. The detection side of the application is solved!
Yet another SWIR application is hyperspectral imaging. By identifying the spectral signature of every pixel in a scene, we can use light to discern the unique profile of substances. This in turn can identify the substance and permit object identification or process detection. Consider also multi-spectral imaging, an efficient sub-mode of hyperspectral imaging that only looks for certain bands sufficient to discern “all that’s needed”.
The SWIR images shown above are pseudo-images, where pixel values in the SWIR spectrum have been re-mapped into the visible spectrum along grey levels. But that’s just to help our understanding, as an automated machine vision application doesn’t need to show an image to a human operator.
In machine vision, an algorithm on the host PC interprets the pixel values to identify features and make actionable determinations. Such as “move apple to juicer” or “continue filling bottle”.
SWIR sensors and cameras; SWIR lighting, and SWIR lenses. For cameras and sensors, consider Allied Vision’s Goldeye series:
Goldeye SWIR cameras are available in compact, rugged, industrial models, or as advanced scientific versions. The former has optional thermal electric cooling (TEC), while the latter is only available in cooled versions.
For SWIR lighting, consider Effilux bar and ring lights. Effilux lights come in various wavelengths for both the visible and SWIR applications. Contact us to discuss SWIR lighting options.
By emitting light in the SWIR range, directed to reflect off targets known to reveal features in the SWIR spectrum, one builds the components necessary for a successful application.
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And don’t forget the lens. One may also need a SWIR-specific lens, or a hybrid machine vision lens that passes both visible and SWIR wavelengths. Consider Computar VISWIR Lite Series Lenses or their VISWIR Hyper-APO Series Lenses. It’s beyond the scope of this short blog to go into SWIR lensing. Read our recent blog on Wide Band SWIR Lensing and Applications or speak with your lensing professional to be sure you get the right lens.
Whether SWIR or UV (more on that another time), the key point is that some machine vision problems are best solved outside the human visible portions of the spectrum. While there are innovative users and manufacturers continuing to push the boundaries – these areas are sufficiently mature that solutions are predictably creatable. Think beyond the visible constraints!
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(Visited 5,020 times, 1 visits today)In machine vision, backlighting is one of the most common and useful lighting techniques. It’s typically used for parts presence/absence, gauging, and orientation/location, particularly when coupled with pick-and-place or vision-guided robotics applications. However, incorporating backlighting into machine vision systems can be difficult, primarily because component placement geometry requires access to the part top and bottom (or front and back) for the camera and backlight.
In this example, we used BL-660 (Red) and BL-880 (Near IR) surface mount backlights on a semi-transparent, populated PCB (Figs. 4a and 4b, respectively) to test how well each wavelength penetrated the material. We see that the 880 nm IR light better defines the traces in the board than the 660 nm Red light. However, how do we know that the IR light wasn’t just more intense with respect to the camera – or even that the camera was more sensitive to IR light?
Examining the board images more closely, we notice a hole in the upper portion of the board. Comparing this to the 880 nm IR image, see how the red (shorter wavelength) light was so bright it bloomed the hole edges. Taking this into consideration, we can conclude that despite the camera being more sensitive to the red light, the IR light clearly penetrated the board better.
A similar penetration trend can be observed in inspecting an incandescent light bulb filament. A series of images taken with 470 nm blue, 660 nm red, and 880 nm IR back lights (Figures 5a, 5b, 5c respectively) illustrate this point. Blue light does not penetrate the light bulb glass and diffuser coatings; the red light shows some filament detail; and ultimately, the IR light clearly produces the most useful inspection detail.
Another common backlighting application is verifying fluid fill-level in bottles. In this example, a colored glass bottle contains clear cologne. If we backlight with red, the most common color, we see that the light does not penetrate the bottle (Figure 6a). Based on the general assumption that a longer wavelength light penetrates better, we switch to IR 880 nm light; notice it still does not penetrate the bottle and its contents (Figure 6b). For the sake of completeness, we attempt blue, the shortest wavelength light; interestingly, it penetrates the bottle and contents well enough to verify the liquid fill height (Figure 6c)
It is important to note that part color has a different effect on contrast levels depending on whether we are inspecting opaque or semi-transparent objects. In the case of opaque objects, we are inspecting with reflected light rather than transmitted light (backlighting), so wavelength-specific color contrast is very important. When backlighting semi-transparent parts, color typically plays a minor role in wavelength-specific differential light transmission through an object, whereas the material’s composition, internal structure, and/or texture are significantly more influential in this regard.
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