Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infr...Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed.This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels.Among them,LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library,while motion frame difference method can detect the moving regions of the image,improve the integrity of target regions such as camouflage,sheltering and deformation.In order to integrate the advantages of the two methods,the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced.The enhancement module of the network strengthened and screened the targets,and realized the background suppression of infrared images.Based on the experiments,the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated,and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets,and it’s detection performance was far better than the comparison algorithm.The experiment results indicated that,compared with traditional infrared target detection methods,the proposed method could detect the infrared invasion target more accurately,and suppress the background noise more effectively.展开更多
外来入侵植物命名实体识别是进一步挖掘入侵植物信息的关键步骤。为解决外来入侵植物领域命名实体识别存在训练数据稀缺、字符级向量表征单一、专业实体识别精度不足等问题,构建了一种基于多特征融合的外来入侵植物细粒度命名实体识别模...外来入侵植物命名实体识别是进一步挖掘入侵植物信息的关键步骤。为解决外来入侵植物领域命名实体识别存在训练数据稀缺、字符级向量表征单一、专业实体识别精度不足等问题,构建了一种基于多特征融合的外来入侵植物细粒度命名实体识别模型(invasive alien plant fine-grained named entity recognition model based on multi-feature fusion,IAPMFF)。首先,采用RoBERTa(Robustly optimized BERT approach,RoBERTa)预训练模型为基础架构,通过构建领域专用词典并通过词汇特征向量融合,增强模型对低频词及专业术语的表征能力;其次,设计双通道特征提取层,利用双向长短时记忆网络(Bi-directional long-short term memory,BiLSTM)提取长序列语义特征,结合卷积残差结构(convolution residual structure,CRS)捕获更多细粒度特征;然后,设计分层特征融合机制,通过多头自注意力机制加权融合两种特征向量,构建多维度语义表征;最后,采用条件随机场(conditional random field,CRF)进行序列解码优化。基于专家知识,构建包含24类细粒度实体标签的外来入侵植物命名实体识别数据集。试验表明,IAP-MFF模型在外来入侵植物命名实体识别数据集上取得91.51%精确率、92.51%召回率和92.01%的F1值,较基线模型分别提升4.40、3.39、3.91个百分点,显著改善了小样本细粒度实体的识别效果。在Weibo、Resume公共数据集上F1值分别达到72.75%和97.15%,表明了模型的泛化性和优越性能。IAP-MFF模型通过融合包含领域知识在内的多种特征,有效提升实体识别精度与泛化能力,为外来入侵植物知识图谱构建奠定技术基础。展开更多
基金This work was supported by the National Natural Science Foundation of China(grant number:61671470)the National Key Research and Development Program of China(grant number:2016YFC0802904)the Postdoctoral Science Foundation Funded Project of China(grant number:2017M623423).
文摘Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed.This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels.Among them,LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library,while motion frame difference method can detect the moving regions of the image,improve the integrity of target regions such as camouflage,sheltering and deformation.In order to integrate the advantages of the two methods,the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced.The enhancement module of the network strengthened and screened the targets,and realized the background suppression of infrared images.Based on the experiments,the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated,and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets,and it’s detection performance was far better than the comparison algorithm.The experiment results indicated that,compared with traditional infrared target detection methods,the proposed method could detect the infrared invasion target more accurately,and suppress the background noise more effectively.
文摘外来入侵植物命名实体识别是进一步挖掘入侵植物信息的关键步骤。为解决外来入侵植物领域命名实体识别存在训练数据稀缺、字符级向量表征单一、专业实体识别精度不足等问题,构建了一种基于多特征融合的外来入侵植物细粒度命名实体识别模型(invasive alien plant fine-grained named entity recognition model based on multi-feature fusion,IAPMFF)。首先,采用RoBERTa(Robustly optimized BERT approach,RoBERTa)预训练模型为基础架构,通过构建领域专用词典并通过词汇特征向量融合,增强模型对低频词及专业术语的表征能力;其次,设计双通道特征提取层,利用双向长短时记忆网络(Bi-directional long-short term memory,BiLSTM)提取长序列语义特征,结合卷积残差结构(convolution residual structure,CRS)捕获更多细粒度特征;然后,设计分层特征融合机制,通过多头自注意力机制加权融合两种特征向量,构建多维度语义表征;最后,采用条件随机场(conditional random field,CRF)进行序列解码优化。基于专家知识,构建包含24类细粒度实体标签的外来入侵植物命名实体识别数据集。试验表明,IAP-MFF模型在外来入侵植物命名实体识别数据集上取得91.51%精确率、92.51%召回率和92.01%的F1值,较基线模型分别提升4.40、3.39、3.91个百分点,显著改善了小样本细粒度实体的识别效果。在Weibo、Resume公共数据集上F1值分别达到72.75%和97.15%,表明了模型的泛化性和优越性能。IAP-MFF模型通过融合包含领域知识在内的多种特征,有效提升实体识别精度与泛化能力,为外来入侵植物知识图谱构建奠定技术基础。
