Design and Implementation of an Automatic White Sugar Impurity Detection System based on Multispectral Imaging
DOI:
https://doi.org/10.54097/36j24175Keywords:
Multi-modal Image Fusion, Attention Unet, Multispectral Images, Impurity Detection, Deep LearningAbstract
Traditional methods for removing impurities from sugar are not very inefficient and have limited accuracy. This study designed and implemented an automatic white sugar impurity detection system based on multi-modal image fusion technology to detect impurities in white sugar efficiently and accurately. The system utilizes 4 multi-spectral cameras and an RGB camera to acquire high-resolution images of the samples. By extending the input channels of the Attention U-Net model to thirteen, the recognition rate of impurity categories is improved by approximately 60%, significantly enhancing the detection performance of the model. The experimental results show that the system performs excellently in the image segmentation task, achieving an average intersection over union (mIoU) of 92.70% and an average pixel accuracy (mPA) of 93.74%. The accuracy for impurity categories reaches 95%. Finally, the model was applied to test the effectiveness in identifying impurities; the results indicate that the model can effectively identify various types of impurities and also perform well in recognizing unknown categories.
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