结合三维卷积神经网络和Haar小波滤波器的高光谱猕猴桃品质分类

Hyperspectral Imaging-Based Quality Classification for Kiwifruit by Incorporating Three-Dimensional Convolution Neural Network and Haar Wavelet Filter

针对猕猴桃硬度品质无损检测分类困难的问题,提出了结合高光谱成像技术和卷积神经网络的分类模型。该模型融合Haar小波核提取的空间特征信息和三维卷积核提取的空谱联合信息,采用分解数据通道连接的方式确保所有特征能够流到模型末尾,提升了网络特征提取的能力。通过自制的猕猴桃硬度品质Kiwi_seed数据集上的实验表明,Haar小波变换模块可以显著提升网络的特征提取能力;通过消融实验表明,在增加Haar小波变换模块后模型的分类准确率提升了7.4%,最优可达97.3%,优于经典的图像分类网络,可以很好地解决猕猴桃硬度品质的无损检测分类问题。

To address challenges in the non-destructive inspection and classification of kiwifruit hardness quality,we propose a classification model that incorporates hyperspectral imaging technology and a convolution neural network.This network combines the spatial feature information extracted by the Haar wavelet and the space-spectrum joint information extracted by the three-dimensional(3D)convolution kernel.In this network,the data decomposition of channel connections is executed to ensure that all features can be utilized by the model,which improves the ability of network feature learning.Experiments on the acquired hyperspectral image-based,self-made kiwifruit hardness quality dataset(named Kiwi_seed)demonstrate that the Haar wavelet transform module can significantly improve the feature extraction ability of the network.Ablation experiments reveal that the classification accuracy of the model incorporating the Haar wavelet transform module is increased by 7.4%and reaches the optimum level at 97.3%,which is better than the classical image classification network.The proposed classification model can be effectively used for the non-destructive inspection and classification of kiwifruit quality.