Enhancing the machine vision performance with multi-spectral light sources
This work addresses incremental improvements in machine vision performance for applications like object recognition by optimizing lighting conditions.
The study investigated how 35 different multi-spectral light sources affect machine vision accuracy on colored pencil images using AlexNet and VGG19, finding that some non-pure white lights outperformed pure white in accuracy, suggesting potential for enhancement.
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.