农业机械学报2024，Vol.55Issue(7)：270-279,324,11.DOI:10.6041/j.issn.1000-1298.2024.07.026
基于改进注意力机制和多语义特征增强的自然环境下枣品种识别方法
Jujube Variety Recognition Based on Improved Attention Mechanism and Multi-semantic Feature Enhancement
摘要
Abstract
In response to the low accuracy of jujube variety recognition in current natural scenarios,a jujube variety recognition model was proposed based on attention mechanism and multi-semantic feature enhancement(ICBAM_MSFE_Res50).On the basis of ResNet-50,the attention mechanism ICBAM(improved convolutional block attention module)was introduced.ICBAM improved the convolutional block attention module(CBAM)by using one-dimensional convolution and multi-scale hole convolution,eliminating information loss during feature map dimensionality reduction,reducing the computational and parameter complexity of the model,and improving the model's ability to extract fine-grained features in jujube fruit regions.At the same time,a multi-semantic feature enhancement(MSFE)module was proposed,which extracted more local salient features of jujube fruit through jujube fruit region localization algorithm,and used saliency feature suppression algorithm to force the model to learn secondary features of jujube fruit,thereby achieving the learning of multiple semantic features of jujube fruit.The experimental results showed that the accuracy of the model on the dataset of 20 types of jujube varieties was 92.20%,which was 4.26 percentage points higher than that of ResNet-50.Compared with the AlexNet,VGG-16,ResNet-18,and InceptionV3 models,the accuracy was improved by 15.84,9.22,6.86,and 3.55 percentage points,respectively.Compared with other jujube variety recognition methods,this method still performed the best in the recognition of 20 types of jujube,which can provide reference for research on jujube variety recognition in natural scenarios.关键词
枣品种识别/深度学习/注意力机制/多语义特征增强Key words
jujube variety recognition/deep learning/attention mechanism/multi-semantic feature enhancement分类
计算机与自动化引用本文复制引用
雷浩,苑迎春,许楠,何振学..基于改进注意力机制和多语义特征增强的自然环境下枣品种识别方法[J].农业机械学报,2024,55(7):270-279,324,11.基金项目
国家自然科学基金项目(62102130)和河北省自然科学基金项目(F2020204003) （62102130）
