计算机工程与应用2024，Vol.60Issue(8)：110-120,11.DOI:10.3778/j.issn.1002-8331.2211-0468
改进Deeplabv3+的双注意力融合作物分类方法
Improved Deeplabv3+ Crop Classification Method Based on Double Attention Fusion
摘要
Abstract
In recent years,convolutional neural networks(CNN)have made new progress in crop classification research,but they have shown some limitations in modeling long-term dependence,and there are deficiencies in capturing the global characteristics of crops.In view of the above problems,Transformer is introduced into the Deeplab v3+ model,and a par-allel branch structure for crop classification of drone images,the DeepTrans(Deeplab v3+ with Transformer)model is pro-posed.DeepTrans combines Transformer and CNN in a parallel way,which is conducive to the effective capture of global and local features.Transformer is introduced to enhance the remote dependence of information in the image and improve the extraction ability of crop global information.Channel attention mechanism and spatial attention mechanism are added to enhance the sensitivity of Transformer to channel information and the ability of ASPP(aerospace spatial pyramid pooling)to capture crop spatial information.The experimental result shows that the MIoU index of the DeepTrans mod-el can reach 0.812,which is 3.9%higher than that of the Deeplab v3+ model.The accuracy of the model in the classifica-tion of five crops has been improved.For sugarcane,corn and banana which are easy to be wrongly classified,their IoU has been increased by 2.9%,4.7%and 13%respectively.It can be seen that DeepTrans model has a better segmentation ef-fect in the internal filling and global prediction of crop classification images,which is helpful to monitor the planting structure and scale of farmland crops more timely and accurately.关键词
农作物分类/无人机影像/Deeplab v3+/Transformer/注意力机制Key words
crop classification/drone image/Deeplab v3+/Transformer/attention module分类
信息技术与安全科学引用本文复制引用
郭金,宋廷强,孙媛媛,巩传江,刘亚林,马兴录,范海生..改进Deeplabv3+的双注意力融合作物分类方法[J].计算机工程与应用,2024,60(8):110-120,11.基金项目
山东省重点研发计划项目(2019GGX101047) （2019GGX101047）
山东省自然科学基金青年项目(ZR2021QC120). （ZR2021QC120）
