重建高光谱图像的酿酒高粱品种识别方法研究

目的 基于重建高光谱图像技术实现对酿酒高粱品种的实时快速识别。方法 对分层回归网络(hierarchical regression network, HRNet)进行改进,得到残差注意力分层回归网络(residual attention-hierarchical regression network, RA-HRNet)。利用该网络进行重建高光谱图像,并在此基础上建立双向长短期记忆网络结合注意力机制(bi-directional long short-term memory-attention, BiLSTM-Attention)的酿酒高粱品种识别模型。以原始RGB数据作为重建高光谱图像网络的输入,将输出重建的光谱图像作为酿酒高粱品种识别模型的输入,以完成酿酒高粱品种识别。结果 RA-HRNet相比HRNet,模型参数量Params(M)]降低80.5%,模型计算量floating point operations per second, FLOPS(G)]降低80.2%,峰值信噪比(peak signal-to-noise ratio, PSNR)提升16.9%,平均相对绝对误差...

Research on the recognition method of sorghum varieties for liquor production by reconstructing hyperspectral images

Objective To achieve real-time and rapid identification of sorghum varieties for liquor production based on reconstructing hyperspectral images technology. Methods The hierarchical regression network (HRNet) was improved to obtain the residual attention-hierarchical regression network (RA-HRNet). Using this network to reconstruct spectral images, and based on this, a bi-directional long short-term memory-attention (BiLSTM- Attention) for liquor sorghum variety recognition model was established. The original RGB data was used as input for the reconstruction spectral image network, and the output reconstructed spectral image was used as input for the sorghum variety identification model, completing the identification of sorghum varieties for liquor production. Results Compared to HRNet, the RA-HRNet model had reduced the number of parameters Params (M)] by 80.5%, reduced the floating point operations per second FLOPS (G)] by 80.2%, improved the peak signal-to-noise ratio (PSNR) by 16.9%, decreased the mean relative absolute error (MRAE) by 31.8% and reduced the root mean squared error (RMSE) by 19.1%. Compared to high spectral detection, the efficiency of reconstructed spectral detection had increased by 95.8%, the recognition accuracy of the sorghum variety identification model for liquor production could reach up to 95.1% at its highest. Conclusion The combination of RA-HRNet reconstruction spectral image network and BiLSTM-Attention model can quickly identify sorghum varieties for liquor production in real time.