{"id":5487,"date":"2026-11-14T10:44:18","date_gmt":"2026-11-14T15:44:18","guid":{"rendered":"https:\/\/www.1stvision.com\/machine-vision-solutions\/?p=5487"},"modified":"2026-11-14T10:44:19","modified_gmt":"2026-11-14T15:44:19","slug":"swir-imaging-the-camera-is-only-half-the-solution","status":"publish","type":"post","link":"https:\/\/www.1stvision.com\/machine-vision-solutions\/2026\/11\/swir-imaging-the-camera-is-only-half-the-solution.html","title":{"rendered":"SWIR Imaging: The Camera Is Only Half the Solution"},"content":{"rendered":"\n

A SWIR camera can reveal features, materials, and defects that are invisible to a conventional machine vision camera. But the camera is only part of the equation. To get useful contrast, you need to illuminate the target at the right SWIR wavelength and with the right lighting geometry.<\/p>\n\n\n\n

That’s where SWIR lighting becomes critical. The right combination of camera, wavelength, and illumination can make an otherwise difficult inspection surprisingly easy.<\/p>\n\n\n

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This teaser image shows three different scenes lit with VIS vs. SWIR light. Read on for details – Courtesy EffiLux<\/figcaption><\/figure>\n<\/div>\n\n\n

SWIR is enjoying its CMOS moment<\/h2>\n\n\n\n

Just as CMOS sensors flirted with CCD and suddenly took on huge market share, early SWIR imaging was seen as rather specialized and confined to niche applications. That’s changing rapidly.<\/p>\n\n\n\n

We’ve written about this in a recent blog<\/a>. And developed a SWIR overview and products section<\/a> on our website.<\/p>\n\n\n\n

Feature identification requires contrast<\/h2>\n\n\n\n

Whether in VIS or SWIR, maximizing contrast<\/a> is essential. Besides choosing a good sensor that’s suited to the resolution requirements and light reflecting\/emitting characteristics of the target object(s), a key factor to control is lighting.<\/p>\n\n\n\n

Ambient light might be sufficient for some applications, but for many it’s either necessary or a clear value-add to provide lighting that really makes the contrast “pop”.<\/p>\n\n\n

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Good SWIR lighting insures spectral matching and homogeneity – Courtesy EffiLux<\/figcaption><\/figure>\n<\/div>\n\n\n
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Exploit different materials’ absorption\/reflection characteristics<\/h2>\n\n\n\n

Materials that are purely or mostly of a certain composition reflect or absorb specific frequencies of light. We can exploit that by matching lighting frequency, sensor, and sometimes “bandpass” or “band-blocking” filters to look for (or exclude) materials. Let’s call it wavelength optimization<\/a>.<\/p>\n\n\n\n

Below see a screenshot from lighting manufacturer EffiLux, showing a variety of wavelength-specific LED light frequencies:<\/p>\n\n\n

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Diverse UV, VIS, and SWIR wavelength lighting options – Courtesy EffiLux<\/figcaption><\/figure>\n<\/div>\n\n\n

Application examples<\/h2>\n\n\n\n
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In this food sorting example foreign objects readily appear in contrast to the grains of rice – when using SWIR imaging and good SWIR lighting. Courtesy EffiLux<\/em><\/p>\n<\/div><\/div>\n\n\n\n

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Verify fill levels in containers opaque to VIS – as SWIR can see through plastic, while water-based solutions appear black in SWIR wavelengths. Courtesy EffiLux.<\/em><\/p>\n<\/div>

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Note: Per the wavelength optimization notes above, water is known to absorb at 1450 nm. Lots of fluids are water-based, so such fluids appear black to an NIR sensor because nothing is reflected back when exposed to 1450 nm light.<\/p>\n\n\n\n

Other fluids have different absorption \/ reflection characteristics. One can look it up, and design a system according to what you want to detect as present or absent.<\/p>\n\n\n\n

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Different LED lights according to application requirements<\/h2>\n\n\n\n