摘要
【目的】基于鸟类影像数据,探讨全域与局域特征融合手段,结合深度卷积神经网络理论,建构鸟类种群识别模型,以期为森林与湿地的监控与治理提供新的手段。【方法】首先,依据人类识别物体从整体到局部的生理过程,采用跳跃结构实现物体整体信息与局部信息的交互,该模型主要采用2个模型框架提取鸟类的全域和局域部件特征,并采用跳跃结构,提出融合模块(Fusion block)结构进行特征融合,将全局特征信息传递至局部特征抽取模块。该模型训练阶段需提供鸟类局部的部位标注信息,而测试阶段采用Faster R-CNN模型自动提取其鸟类局部标注信息。其次,探讨不同鸟类局部影像信息对模型的影响,最后,通过对比不同网络分类模型和鸟类数据集,验证模型的有效性和适用性。【结果】该鸟类种群分类模型具有较高的分类精度,总体分类精度达90%以上;对于不同的鸟类局部影像信息,其分类精度表现出一定的差异性,其中基于鸟类头部局部影像的网络分类模型总体分类精度最高;Faster R-CNN模型对鸟类局部影像定位精度较高,测试阶段采用人工标注的局部影像标签和Faster R-CNN模型预测的局部影像标签对模型的总体分类精度差异小;对比Inception-V1、Res Net-101、Dense Net-121以及Bilinear CNN等网络分类模型总体分类精度,该模型总体分类精相对较高,具有一定的有效性;对比使用NABirds鸟类数据集的分类效果,该模型总体分类表现较好,具有一定的适用性。【结论】该鸟类种群分类模型具有较好的识别效果以及有效性,可为森林与湿地的监控和治理提供合理有效的依据。
【Objective】In this study,based on the bird images,we construct a bird population identification model with the deep convolutional neural network theory by combining the global and local features fusion method,in order to provide a new approach for monitoring and management of forests and wetlands.【Method】First of all,according to the physiological process of the object identification of human from entireness to part,the jump structure was used to implement the interaction between global and local information.In the proposed model,two model frameworks are mainly used to extract the global and local features of birds,and the jump structure is used to propose the fusion module structure for feature fusion,which transfers the global feature information to local feature extraction module.In the training stage of the model,we need to provide the labeling information on the local parts of birds,while in the test stage,we use Faster R-CNN model to automatically extract the labeling information on the local parts of birds.Secondly,we discussed the effects of different bird local image information on the model.Finally,the validity and applicability of the model are verified by comparing different network classification models and bird datasets.【Result】The bird species classification model proposed in this paper has high classification accuracy,and the overall classification accuracy is over 90%.For the image information of different parts of a bird,the classification accuracy of the model shows a certain difference,among which the overall classification accuracy of the network classification model based on the bird’s head image is the highest.The Faster R-CNN model has a high accuracy in bird part image locating.There is little difference in the overall accuracy between the manually labeled local image tag and the local image tag predicted by Faster R-CNN model in the test stage.Compared with the overall classification accuracy of the network classification models such as Inception-V1、ResNet-101、DenseNet-121 and Bilinear CNN,the overall classification accuracy of the model proposed in this paper is relatively high,that verifies the effectiveness of the proposed classification model of bird.Compared with the classification accuracy by using NABirds bird dataset,the overall classification performance of the proposed model is better,which verifies the applicability of the proposed model.【Conclusion】The proposed bird species classification model has good identification results and effectiveness,which can provide a reasonable and effective basis for monitoring and management of forests and wetlands.
作者
林志玮
丁启禄
刘金福
Lin Zhiwei;Ding Qilu;Liu Jinfu(College of Computer and Information Science,Fujian Agriculture and Forestry University ,Fuzhou 350002;College of Forestry,Fujian Agriculture and Forestry University ,Fuzhou 350002;Forestry Post-Doctoral Station of Fujian Agriculture and Forestry University ,Fuzhou 350002;Key Laboratory for Ecology and Resource Statistics of Fujian Province ,Fuzhou 350002;Cross-Strait Nature Reserve Research Center,Fujian Agriculture and Forestry University ,Fuzhou 350002;Fuzhou Central Branch of People’s Bank of China ,Fuzhou 350003)
出处
《林业科学》
EI
CAS
CSCD
北大核心
2020年第1期133-144,共12页
Scientia Silvae Sinicae
基金
教育部人文社会科学研究项目(18YJCZH093)
福建省林业科学研究项目(KH1701390)
海峡博士后交流资助计划
中国博士后科学基金面上项目(2018M632565)
关键词
鸟类种群识别
多框架深度神经网络
全域与局域特征
bird identification
deep convolutional neural network
global and local components
