| dc.description.abstract | Abstract
Image recognition is the core technology of industrial vision detection, and lighting and color temperature are often one of the most important reasons to affect identification performance. Correct color temperature estimation can effectively improve image recognition performance. The traditional solution focuses on how to compensate the image chroma through the algorithm under a specific light source. In this study, an intelligent visual sensor with positive color temperature is designed, and a neural network color correction model is established by six spectral reflection signals of spectral sensors, which can provide good color constancy by image in different illumination environments. We have realized the functions of image, spectral measurement, neural network color correction, and light control in the embedded platform of ARM Cortex-M7, and completed an intelligent visual sensing system which can automatically and more positive color temperature. Finally, we verify this visual sensing system by testing the baking degree of coffee beans. This system does not need to compensate the image chroma through the complex algorithm also does not need to take multiple images to compare the difference, only needs to implement the ambient color temperature correction before the use can achieve excellent image identification results. For the detection environment with serious chromatic aberration variation, our system can also achieve good reliability of detection, which can be applied to a wide range of visual inspection. | en_US |