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.

该研究主要关注多光谱光源在机器视觉中对不同物体颜色的性能，并尝试通过多光谱光源增强机器视觉。使用不同颜色的铅笔作为样本，通过使用两个经典神经网络 AlexNet 和 VGG19 识别收集的图像，研究了在 35 种不同的多光谱光源下的性能。结果表明，对于两个模型来说，总是有一些非纯白光源，其准确性优于纯白光，这表明了多光谱光源进一步增强机器视觉的潜力。还进行了两个模型的比较，令人惊讶地发现 VGG19 的整体性能低于 AlexNet，这表明了多光谱光源和模型选择的重要性。