AUNet: a deep learning method for spectral information classification to identify inks.

It is common to tamper with the contents of documents and forge contracts illegally. In this work, we propose a U-shaped network with attention modules (AUNet) and combine it with a hyperspectral system to effectively identify different inks. It provides an effective detection method for illegal tampering with documents and forging contract contents. First, the hyperspectral system obtains the spectral information of different pen inks without destroying the sample. Second, because the hyperspectral system's detection data have the characteristics of small samples, we introduce U-Net to conduct the deep fusion of multi-level spectral information to avoid feature degradation and fully mine the deep features hidden in the spectral information. Finally, spatial and channel attention modules are introduced to focus on the features affecting classification performance. The results show that AUNet effectively realizes the effective classification of ink spectral information and achieves 97.81% accuracy, 98.71% recall, 98.80% precision, and 98.71% F1-score.

A residual dense comprehensively regulated convolutional neural network to identify spectral information for egg quality traceability.

In the egg market, due to the different nutritional values of eggs laid by hens under different feeding conditions, it is common for low-quality eggs to be counterfeited as high-quality eggs. This paper proposes a residual dense comprehensively regulated convolutional neural network (RDCR-Net) to identify the quality of eggs laid by hens under different feeding conditions. Firstly, a hyperspectral system is used to obtain the spectral information of eggs. Secondly, due to the complex structure of the spectral information, a comprehensively regulated convolution (CRConv) is proposed to extract features hidden in the spectral information through feature transformation in multiple spaces. Thirdly, due to the limited availability of spectral information training samples, deep networks may suffer from feature degradation. The residual dense comprehensively regulated block (RDCR-Block) is proposed to tightly connect multiple CRConv layers with residual dense connections. Finally, the RDCR-Block is taken as the central unit, and the RDCR-Net is designed to identify egg spectral information. In the comparison of multi-model results, the RDCR-Net obtains the best classification performance with 96.29% accuracy, 97.53% precision, 97.14% recall, and 96.19% kappa coefficient. In summary, the RDCR-Net effectively extracts the deep features of spectral information, achieves high accuracy in identifying eggs laid by hens under different feeding conditions, and provides a new method for egg quality traceability.