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Enhancing the machine vision performance with multi-spectral light sources

arXiv 2311.06276 ↗First posted 20 Oct 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 20 Oct 2023.

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References · 14

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2 more
  1. 2018How is Contrast Encoded in Deep Neural Networks?cited by 1
  2. 2019Manifestation of Image Contrast in Deep Networkscited by 1

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