In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-con...In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-consuming.Most nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status data.Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model.Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep learning.Since desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang.The results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,respectively.Considering the demand factors of hardware equipment and inference time,Mobile NetworkV2 achieves the best balance among the Accuracy,the number of parameters and the number of floating-point operations.The number of parameters for Mobile Network V2(MobileNetV2)is 1/16 of RegNetX_8GF,and the number of floating-point operations is 1/24.Our findings can facilitate efficient decision-making for the management of species survey,cataloging,inspection,and monitoring in the nature reserves in Xinjiang,providing a scientific basis for the protection and utilization of natural plant resources.展开更多
在无人机的个体识别中,针对现有的识别方法存在分类准确率低、实时性差、网络模型参数量大以及难以应用于资源受限的设备等问题,提出基于轻量级残差网络(scale down resnet,SDRNet)的无人机个体识别方法。首先,通过短时傅里叶变换(short...在无人机的个体识别中,针对现有的识别方法存在分类准确率低、实时性差、网络模型参数量大以及难以应用于资源受限的设备等问题,提出基于轻量级残差网络(scale down resnet,SDRNet)的无人机个体识别方法。首先,通过短时傅里叶变换(short time fourier transform,STFT)把一维时域信号转为二维时频图像,并对图像进行灰度处理。其次,网络基于MobileNetv2,结合无人机个体信号的时频图像特征,通过缩减网络层数以降低模型深度,通过减少输出通道数以降低模型维度;借鉴ResNet残差结构的设计思想,通过增加卷积层的残差连接以实现更多不同深度网络的集成,设计出轻量级残差网络SDRNet模型。最后,利用STFT时频灰度图作为样本对SDRNet模型进行训练,实现对无人机个体的识别。仿真实验采用公开的6架悬停无人机的信号、在AWGN高斯白噪声信道环境下进行。实验结果表明,所提方法在信噪比SNR=10 dB的环境下对无人机个体信号的平均识别率为94.00%,分别高于MobileNetv2、GoogleNet模型的0.17%、5.17%,低于ResNet模型2.50%;所设计的SDRNet模型的可学习参数量、模型规模、乘加计算量约为基础轻量级MobileNetv2模型的19.5%、19.6%、35.9%。相较于基于MobileNetv2、GoogleNet、ResNet等神经网络模型方法相比,所提方法在保持较高识别准确率的同时,具有更快的识别速度和更小的内存开销。展开更多
基金supported by the West Light Foundation of the Chinese Academy of Sciences(2019-XBQNXZ-A-007)the National Natural Science Foundation of China(12071458,71731009).
文摘In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-consuming.Most nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status data.Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model.Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep learning.Since desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang.The results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,respectively.Considering the demand factors of hardware equipment and inference time,Mobile NetworkV2 achieves the best balance among the Accuracy,the number of parameters and the number of floating-point operations.The number of parameters for Mobile Network V2(MobileNetV2)is 1/16 of RegNetX_8GF,and the number of floating-point operations is 1/24.Our findings can facilitate efficient decision-making for the management of species survey,cataloging,inspection,and monitoring in the nature reserves in Xinjiang,providing a scientific basis for the protection and utilization of natural plant resources.
文摘在无人机的个体识别中,针对现有的识别方法存在分类准确率低、实时性差、网络模型参数量大以及难以应用于资源受限的设备等问题,提出基于轻量级残差网络(scale down resnet,SDRNet)的无人机个体识别方法。首先,通过短时傅里叶变换(short time fourier transform,STFT)把一维时域信号转为二维时频图像,并对图像进行灰度处理。其次,网络基于MobileNetv2,结合无人机个体信号的时频图像特征,通过缩减网络层数以降低模型深度,通过减少输出通道数以降低模型维度;借鉴ResNet残差结构的设计思想,通过增加卷积层的残差连接以实现更多不同深度网络的集成,设计出轻量级残差网络SDRNet模型。最后,利用STFT时频灰度图作为样本对SDRNet模型进行训练,实现对无人机个体的识别。仿真实验采用公开的6架悬停无人机的信号、在AWGN高斯白噪声信道环境下进行。实验结果表明,所提方法在信噪比SNR=10 dB的环境下对无人机个体信号的平均识别率为94.00%,分别高于MobileNetv2、GoogleNet模型的0.17%、5.17%,低于ResNet模型2.50%;所设计的SDRNet模型的可学习参数量、模型规模、乘加计算量约为基础轻量级MobileNetv2模型的19.5%、19.6%、35.9%。相较于基于MobileNetv2、GoogleNet、ResNet等神经网络模型方法相比,所提方法在保持较高识别准确率的同时,具有更快的识别速度和更小的内存开销。
