基于改进Xception的槟榔分类算法

Betel nut classification algorithm based on improved Xception

目的:降低槟榔分类对人工的需求,同时提升槟榔分类的准确度和缩小分类模型。方法:以扩充Xception的输入层作为特征提取骨干网络;在特征提取网络后加入双路压缩激励模块;使用ELU激活函数代替ReLU;使用数据增强扩充槟榔的数量,将数据集以9∶3∶1划分为训练集、验证集和测试集,用于训练改进Xception模型。结果:使用改进的Xception对测试集1100张槟榔图像分类时达到了99.182%的分类准确度,同时模型大小仅有15.7 MB。结论:改进后的模型能够满足对槟榔分类的准确度和模型大小的要求。

Objective:In order to reduce the manual demand of betel nut classification improve the accuracy of betel nut classification and reduce the size of classification model.Methods:Expanded the input layer of Xception as the feature extraction backbone network.Added a dual-channel sequeeze and excitation module after the feature extraction network.Used the ELU activation function instead of ReLU.Used the data enhancement to expand the dataset of betel nuts,divided the dataset into training sets,validation sets and test sets in 9∶3∶1,and trained the improved Xception models.Results:When the improved Xception was used to classify 1100 betel nut images in the test set,the classification accuracy reached 99.182%,and the model size was 15.7 MB.Conclusion:The improved model can meet the accuracy requirements and model size requirements for betel nut classification.