TECH FOCUS: HYPERSPECTRAL IMAGING


out quality in the least expensive way’


which an automated system can offer. Waste Robotics builds its own scanners


incorporating multiple sensors, including RGB, 3D and hyperspectral cameras, and uses AI to make sense of the data. Te company develops tailored robotic solutions for different types of waste. It has several projects deployed in Canada and the US, as well as France; lines include those for sorting different plastics, as well as construction and demolition debris. Hyperspectral imaging gives Waste


Robotics the ability to differentiate between different types of plastic, or to sort construction material, like wood, depending on whether it has glue or paint on it. It gives additional information that other imaging systems don’t provide. Camirand said that near-infrared optical


sorters have been used to separate waste in combination with air jets to eject material, but optical sorters can’t distinguish between objects. Containers made of multiple plastics, for example, can be identified as individual containers with hyperspectral imaging and put aside for further processing. Tese containers are really difficult to recycle, so they are removed from the line to stop cross-contamination of other plastics. ‘Sorting plastics is difficult,’ Camirand


said. One problem is that packaging changes. ‘If I train an RGB-based AI model –


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A brand of Balluff Steps for spectral imaging success


During our recent webinar, Steve Kinney, director of engineering at Smart Vision Lights, gave his advice on designing a spectral imaging solution for factories. Firstly, he said that multispectral is more practical for production environments, as it reduces the complexity of hyperspectral imaging. To move from


hyperspectral to multispectral, the first step is to start with a full hyperspectral image of the sample and background. Compare each sample’s relative intensity, and compare the peaks in common axes over normalised ranges. Then overlay the data to identify peaks and contrast of unique areas. Step two is to identify


the spectral peaks of interest which, if you’re lucky, might only be one peak. Identifying bruising on an apple, for example, is relatively straightforward and might only require one wavelength band. Sometimes one peak is


not enough – for example, when imaging different species of tree in a forest canopy. Here, additional information might be needed to highlight the different plants. Optical filters can isolate areas of interest from the rest of the spectrum. Multiple bandpass wavelengths can be built into one filter. The aim is to achieve high transmission and steep cut-off to isolate the narrowband edges. Kinney suggests an optical density of five


for band-pass filters. Broadband lighting – halogen or sunlight, for example – is more flexible in combination with narrowband filters, but it requires enough light energy across the target spectrum. Broadband lighting also introduces heat, and there can be loss of spectral efficiency outside of the spectral peaks, so the dynamic range of the camera is dominated by light outside the wavelengths of interest. LED lighting, such as


that offered by Smart Vision Lights, is a more targeted alternative to a broadband illumination source. It has the advantage of higher intensity in the wavelength bands of interest, and might mean filters aren’t necessary.


‘It’s all about sorting


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