改进DeepLabv3+模型无人机图像农田信息提取

doi:10.3778/j.issn.1002-8331.2203-0182

摘要/Abstract

摘要： 自乡村振兴战略实施以来，无人机作为一种高科技工具为我国智慧农业生产发挥着重要作用。但存在信息提取的精准度不高的问题，特别是对农田边缘和小农田的信息提取精度不高。针对该问题，提出一种以GhostNet为骨干网络的改进DeepLabv3+模型的研究方法。该方法将从骨干网络中提取的特征通过特征金字塔网络（FPN）加强特征提取；将编码器中空洞空间金字塔池化模块（ASPP）的1×1卷积替换成空间感知独立自注意层并将ASPP模块中的扩张率进行一定调整，以提高农田边缘的提取精度；为进一步融合多尺度信息将解码器进行上述同样操作；在不降低模型性能的前提下，将特征堆叠（Concatenate）用特征融合（Add）进行替换，以减少模型的训练参数。实验结果表明，改进DeepLabv3+模型平均交并比（mIoU）可达94.57%，平均像素精度（mPA）可达97.16%，相比于DeepLabv3+模型分别提高了4.53%和2.93个百分点，有效提高了农田边缘和小农田的信息提取精度。

关键词: 无人机, 农田信息, 提取, 改进DeepLabv3+, 语义分割

Abstract: Since the implementation of the rural revitalization strategy, UAVs have played an important role in China’s smart agricultural production as a high-tech tool. However, the accuracy of information extraction is not hight, especially for the edge of farmland and small farmland. Aiming at this problem, an improved DeepLabv3+ model with GhostNet as the backbone network is proposed. This method strengthens feature extraction from the backbone network through feature pyramid network（FPN）. Then, this paper replaces the convolution of the atrous spatial pyramid pooling module（ASPP） in the encoder with a spatially aware stand-alone self-attention layer and adjusts the dilated rate in the ASPP module to improve the extraction accuracy of the farmland edge. In order to further fuse multi-scale information, the decoder performs the same operation as described above. Finally, without reducing the performance of the model, the Concatenate method is replaced with Add to reduce the training parameters of the model. The experimental results show that the mean intersection over union （mIOU） and mean pixel accuracy （mPA） of improved DeepLabv3+ model are 94.57% and 97.16%, which are 4.53 and 2.93 percentage points higher than DeepLabv3+ model respectively, which effectively improves the accuracy of farmland information extraction of farmland edge and small farmland.

Key words: UAV, farmland information, extraction, improved DeepLabv3+, semantic segmentation

陈雨情, 王修信. 改进DeepLabv3+模型无人机图像农田信息提取[J]. 计算机工程与应用, 2023, 59(12): 217-227.

CHEN Yuqing, WANG Xiuxin. Improved DeepLabv3+ Model UAV Image Farmland Information Extraction[J]. Computer Engineering and Applications, 2023, 59(12): 217-227.

参考文献

[1] RONNEBERGER O，FISCHER P，BROX T.U-Net：convolutional networks for biomedical image segmentation[C]//Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention，2015.
[2] SHELHAMER E，LONG J，DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015.
[3] ZHAO H S，SHI J P，QI X J，et al.Pyramid scene parsing network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：6230-6239.
[4] CHEN L C，PAPANDREOU G，SCHROFF F，et al.Rethinking atrous convolution for semantic image segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017.
[5] CHEN L C，ZHU Y K，PAPANDREOU G，et al.Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the European Conference on Computer Vision，2018：8-14.
[6] POUDEL PK R，LIWICKI S，CIPOLLA R.Fast-SCNN：fast semantic segmentation network[C]//Proceedings of the British Machine Vision Conference，2019.
[7] SHI H Y，LIN G S，WANG H，et al.SpSequenceNet：semantic segmentation network on 4D point clouds[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2020.
[8] LIU Z，LIN Y T，CAO Y，et al.Swin transformer：hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE International Conference on Computer Vision，2021.
[9] LIU Z，HU H，LIN Y T，et al.Swin transformer V2：scaling up capacity and resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2022.
[10] 彭要奇，肖颖欣，郑永军，等.无人机光谱成像技术在大田中的应用研究进展[J].光谱学与光谱分析，2020，40（5）：1356-1361.
PENG Y Q，XIAO Y X，ZHENG Y J，et al.Research progress in the application of UAV spectral imaging technology in field[J].Spectroscopy and Spectral Analysis，2020，40（5）：1356-1361.
[11] 鲁恒，付萧，贺一楠，等.基于迁移学习的无人机影像耕地信息提取方法[J].农业机械学报，2015，46（12）：274-284.
LU H，FU X，HE Y N，et al.Cultivated land information extraction from high resolution UAV images based on transfer learning[J].Transactions of the Chinese Society for Agricultural Machinery，2015，46（12）：274-284.
[12] 孙钰，韩京冶，陈志泊，等.基于深度学习的大棚及地膜农田无人机航拍监测方法[J].农业机械学报，2018，49（2）：133-140.
SUN Y，HAN J Y，CHEN Z B，et al.Monitoring method for UAV image of greenhouse and plastic-mulched landcover based on deep learning[J].Transactions of the Chinese Society for Agricultural Machinery，2018，49（2）：133-140.
[13] 宁纪锋，倪静，何宜家，等.基于卷积注意力的无人机多光谱遥感影像地膜农田识别[J].农业机械学报，2021，52（9）：213-220.
NING J F，NI J，HE Y J，et al.Convolutional attention based plastic mulching farmland identification via UAV multispectral remote sensing image[J].Transactions of the Chinese Society for Agricultural Machinery，2021，52（9）：213-220.
[14] 邓泓，杨滢婷，刘兆朋，等.基于深度学习的无人机水田图像语义分割方法[J].中国农机化学报，2021，42（10）：165-172.
DENG H，YANG Y T，LIU Z P，et al.Semantic segmentation of paddy image by UAV based on deep learning[J].Journal of Chinese Agricultural Mechanization，2021，42（10）：165-172.
[15] 慕涛阳，赵伟，胡晓宇，等.基于改进DeepLabV3+模型结合无人机遥感的水稻倒伏识别方法[J].中国农业大学学报，2022，27（2）：143-154.
MU T Y，ZHAO W，HU X Y，et al.Rice lodging recognition method based on UAV remote sensing combined with the improved DeepLabV3+ model[J].Journal of China Agricultural University，2022，27（2）：143-154.
[16] 任鸿杰，刘萍，岱超，等.改进DeepLabV3+网络的遥感影像农作物分割方法[J].计算机工程与应用，2022，58（11）：215-223.
REN H J，LIU P，DAI C，et al.Crop segmentation method of remote sensing image based on improved DeepLabv3+ network[J].Computer Engineering and Applications，2022，58（11）：215-223.
[17] CHEN L C，PAPANDREOU G，KOKKINOS I，et al.DeepLab：semantic image segmentation with deep convolutional nets，atrous convolution，and fully connected CRFs[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2018，40（4）：834-848.
[18] HAN K，WANG Y H，TIAN Q，et al.GhostNet：more features from cheap operations[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2020：1580-1589.
[19] IOFFE S，SZEGEDY C.Batch normalization：accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on Machine Learning，2015：448-456.
[20] SINGH S，KRISHNAN S.Filter response normalization layer：eliminating batch dependence in the training of deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2020：11237-11246.
[21] LIN T Y，DOLLáR P，GIRSHICK R，et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition（CVPR），2017：2117-2125.
[22] RAMACHANDRAN P，PARMAR N，VASWANI A，et al.Stand-alone self-attention in vision models[C]//Proceedings of the 33rd Conference on Neural Information Processing Systems，2019：1-13.
[23] GOYAL P，DOLLáR P，GIRSHICK R，et al.Accurate，large minibatch SGD：training imagenet in 1 hour[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：1-12.