一种结合双注意力机制和层次网络结构的细碎农作物分类方法

doi:10.11896/jsjkx.210200169

摘要/Abstract

摘要： 细碎农作物由于单一样本的尺寸较小,单一样本之间具有一定的差异性,不能代表整个样本的特征,并且同种样本的不同等级在形状和颜色上非常相似,使得细碎农作物图像识别具有非常大的挑战性。目前,对干茶叶、大米、大豆等细碎农作物的图像分类方法的研究较为匮乏,并且研究数据集大多是在实验室环境下使用专业的设备进行拍摄的,这给实际应用带来了困难。为此,提出了一种使用手机对细碎农作物样本进行图像采集和处理的方案,并以茶叶和大米样本为例,设计了一种结合双注意力机制的层次网络结构,通过粗粒度-细粒度的分类过程,先进行粗粒度分类,即样本的不同类别,然后结合注意力机制,使网络更加关注同种类别下不同等级的样本之间的差异,从而更精确地对样本进行等级分类。最后,所提方法在采集的数据集上达到了93.9%的识别精度。

关键词: 层次网络结构, 卷积神经网络, 图像分类, 细碎农作物, 注意力机制

Abstract: The image recognition of small crops is very challenging for several reasons.First,the crop is small in size and a single sample is not representative of a collection.Second,different categories or different grades of the same crop may look very similar in shapes and colors.At present,there is a lack of research on image classification methods for small crops such as dried tea,rice and soybean,and most of the research datasets are taken in the laboratory environment with professional equipment,which brings difficulties to the practical application.For this,a method for image acquisition and processing of small crop samples using mobile phones is proposed.By taking tea and rice as a case study,we design a hierarchical network structure combined with two attention mechanisms.Through the coarse-grained to fine-grained classification process,coarse-grained classification is made first,namely different categories of samples,and then combined with two attention mechanisms,the network pays more attention to the diffe-rences between similar samples of different grades under the same category,so that they can be more accurate to classification of samples.Finally,the proposed method achieves the accuracy of 93.9% on the collected datasets.

Key words: Attention mechanism, Convolutional neural network, Hierarchical network structure, Image classification, Small crops

中图分类号:

TP301.6

杨健楠, 张帆. 一种结合双注意力机制和层次网络结构的细碎农作物分类方法[J]. 计算机科学, 2022, 49(6A): 353-357. https://doi.org/10.11896/jsjkx.210200169

YANG Jian-nan, ZHANG Fan. Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure[J]. Computer Science, 2022, 49(6A): 353-357. https://doi.org/10.11896/jsjkx.210200169

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