基于统一网络架构的多模态航空影像质量评价研究  

Research on multimodal aerial image quality evaluation based on unified network architecture

作　　者：闫婧 武林伟[1] 刘伟杰[1] 韩如雪 YAN Jing;WU Linwei;LIU Weijie;HAN Ruxue(The 27th Research Institute of China Electronics Technology Group Corporation,Zhengzhou 450047,China)

机构地区：[1]中国电子科技集团公司第二十七研究所,河南郑州450047

出　　处：《现代电子技术》2023年第17期43-47,共5页Modern Electronics Technique

摘　　要：高质量无人机航空影像是目标检测、分析、识别的重要前提条件,但各类传感器成像机理不同,质量影响因素多样,往往需要根据不同模态数据的特性设计不同的网络模型,从而大大增加了质量评价算法在无人机上的应用难度。针对这一问题,提出一种基于统一网络框架的无参考多模态影像质量评价模型,通过自适应地学习图像块内部的局部特征与图像块之间的相互关系,完成空间维度上的全局信息融合和时间维度上的时序信息融合,实现对多种模态影像数据的质量评估,进而快速有效地监测筛选采集数据的质量,提高有效数据采集效率。实验结果表明,该方法在多种模态的影像数据质量评价上具备泛用性和有效性。High⁃quality UAV aerial image is an important prerequisite for target detection,analysis and recognition.However,the imaging mechanism of various sensors is different,and the factors that may have impact on quality are various.It is necessary to design different network models according to the characteristics of different modal data,so the application of quality evaluation algorithm on UAV has become difficult greatly.In view of this,a no⁃reference multimodal image quality evaluation model based on the unified network framework is proposed.In this model,by adaptive learning of the local features within the image block and the relationship among the image blocks,the global information fusion in the spatial dimension and the temporal information fusion in the time dimension are completed,so as to achieve the quality evaluation of multimodal image data,and then quickly and effectively monitor the quality of the collected data,and improve the efficiency of effective data collection.The experimental results show that the method is of universality and effectiveness in the quality evaluation of multi⁃modal image data.

关 键 词：深度学习 无参考模型 网络结构 多模态数据 影像质量评价 卷积神经网络 特征提取 特征融合 

分 类 号：TN911.73-34[电子电信—通信与信息系统] TP391.41[电子电信—信息与通信工程]