Enhancing the machine vision performance with multi-spectral light
  sources
Feng Zhang Rui Bao Congqi Dai Wanlu Zhang Shu Liu Ruiqian Guo
This study mainly focuses on the performance of different multi-spectral
light sources on different object colors in machine vision and tries to enhance
machine vision with multi-spectral light sources. Using different color pencils
as samples, by recognizing the collected images with two classical neural
networks, AlexNet and VGG19, the performance was investigated under 35
different multi-spectral light sources. The results show that for both models
there are always some non-pure white light sources, whose accuracy is better
than pure white light, which suggests the potential of multi-spectral light
sources to further enhance the effectiveness of machine vision. The comparison
of both models is also performed, and surprised to find that the overall
performance of VGG19 is lower than that of AlexNet, which shows that the
importance of the choice of multi-spectral light sources and models.