Paper 14085-49
Development and evaluation of a new line illumination for hyperspectral imaging systems for industrial applications
Abstract
Hyperspectral line-scan systems are widely employed in industrial sorting applications to distinguish materials based on subtle spectral characteristics, or spectral fingerprints, that are imperceptible to the human eye. Common applications include contaminant detection in food processing and the separation of different plastic types. These systems are well suited for high-throughput inspection due to their ability to capture spatial and spectral information line by line. In such systems, a narrow line across the sample, perpendicular to the direction of relative motion, is projected onto an imaging spectrograph, where the spectral information of each spatial pixel is dispersed along the second axis of a two-dimensional detector. Consequently, only a line-shaped region of the sample requires illumination. Conventional illumination configurations employ multiple halogen spotlights that produce overlapping elliptical illumination areas. While this approach ensures coverage of the region of interest, it results in non-uniform intensity distribution along the scan line and illuminates a larger area than necessary. In this work, we propose an alternative illumination concept utilizing readily available automotive H7 halogen headlight bulbs in combination with an elliptical reflector and a lens array. The performance of the proposed system is compared to that of conventional halogen spot illumination in terms of intensity uniformity and spectral performance.
Presenter
Thomas Arnold
Silicon Austria Labs GmbH (Austria)
Dr. Thomas Arnold is an experienced researcher with over 20 years of expertise in the development of photonic systems and sensor technologies. He holds a PhD in Technical Mathematics and has a background in medical information technology.
He is currently a Staff Scientist at Silicon Austria Labs, where his research focuses on spectroscopy, multi- and hyperspectral imaging, machine learning, and computer vision. His work is centered on advancing pattern recognition methods and photonic sensor technologies for a wide range of applications, bridging fundamental research with practical implementation.