Material classification through distance aware multispectral data fusion

Safety applications require fast, precise and highly reliable sensors at low costs. This paperpresents signal processing methods for an active multispectral optical point sensorinstrumentation for which a first technical implementation exists. Due to the very demandingrequirements for safeguarding equipment, these processing methods are targeted to run on asmall embedded system with a guaranteed reaction time T < 2 ms and a sufficiently lowfailure rate according to applicable safety standards, e.g., ISO-13849. The proposed dataprocessing concept includes a novel technique for distance-aided fusion of multispectral datain order to compensate for displacement-related alteration of the measured signal. Thedistance measuring is based on triangulation with precise results even for low-resolutiondetectors, thus strengthening the practical applicability. Furthermore, standard components,such as support vector machines (SVMs), are used for reliable material classification. Allmethods have been evaluated for variants of the underlying sensor principle. Therefore, theresults of the evaluation are independent of any specific hardware.