针对同步定位与地图建立(simultaneous localization and mapping,SLAM)算法在动态环境下存在位姿估计和地图构建误差较大的问题,提出一种光流语义分割方法用于增加动态场景下的可运行性。将ORB-SLAM2系统与YOLOv5模型结合,对传入图像...针对同步定位与地图建立(simultaneous localization and mapping,SLAM)算法在动态环境下存在位姿估计和地图构建误差较大的问题,提出一种光流语义分割方法用于增加动态场景下的可运行性。将ORB-SLAM2系统与YOLOv5模型结合,对传入图像提取特征点的同时将YOLOv5网络模型语义分割后的物体分为高、中、低动态物体。利用运动一致性检测算法,对三种检测物体动态阈值判断,辨别其是否需要剔除特征点,增加ORB-SLAM2算法在动态环境下的运行精度。为加快系统运行速度,用LK光流法加快普通帧与普通帧之间的匹配,其原理为使用LK光流匹配特征点代替ORB特征点匹配,大大的缩小运行时间,同时运行误差变化不大。实验在TUM数据集下测试,平均每一帧提取2000个特征点,在增加LK光流后缩短0.01 s以上,若在900帧数据集下,可缩短9 s.其绝对轨迹误差对比于ORB-SLAM2和DS-SLAM平均提升在95%与30%以上,证明了算法在动态场景下良好的运行精度与鲁棒性。展开更多
Particles occur in almost all processes in chemical and life sciences. The particle size and shape influence the process performance and product quality, and in turn they are influenced by the flow behavior of the par...Particles occur in almost all processes in chemical and life sciences. The particle size and shape influence the process performance and product quality, and in turn they are influenced by the flow behavior of the particles during production. Monitoring and controlling such characteristics in multiphase systems to obtain sufficient qualities will greatly facilitate the achievement of reproducible and defined distributions. So far, obtaining this information inline has been challenging, because existing instruments lack measurement precision, being unable to process overlapping signals from different particle phases in highly concentrated multiphase systems. However, recent advances in photo-optics made it possible to monitor such features(particle size distribution(PSD), aspect ratio and particle concentration) with advanced image analysis(IA) in real-time. New analysis workflows as well as single feature extractions from the images using multiple image analysis algorithms allowed the precise real-time measurements of size, shape and concentration of particle collectives even separated from each other in three phase systems. The performances, advantages and drawbacks with other non-photo-optical methods for assessing the particle size distribution are compared and discussed.展开更多
Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms,presenting risks for numerous countries,societies,and individuals,and posing a serious threat to cyberspa...Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms,presenting risks for numerous countries,societies,and individuals,and posing a serious threat to cyberspace security.To address the problem of insufficient extraction of spatial features and the fact that temporal features are not considered in the deepfake video detection,we propose a detection method based on improved CapsNet and temporal–spatial features(iCapsNet–TSF).First,the dynamic routing algorithm of CapsNet is improved using weight initialization and updating.Then,the optical flow algorithm is used to extract interframe temporal features of the videos to form a dataset of temporal–spatial features.Finally,the iCapsNet model is employed to fully learn the temporal–spatial features of facial videos,and the results are fused.Experimental results show that the detection accuracy of iCapsNet–TSF reaches 94.07%,98.83%,and 98.50%on the Celeb-DF,FaceSwap,and Deepfakes datasets,respectively,displaying a better performance than most existing mainstream algorithms.The iCapsNet–TSF method combines the capsule network and the optical flow algorithm,providing a novel strategy for the deepfake detection,which is of great significance to the prevention of deepfake attacks and the preservation of cyberspace security.展开更多
针对目前主流SLAM(同时定位与建图)算法在动态环境中存在精度大幅下降的问题,提出了一种基于光流分割去除动态物体干扰的DY-SLAM(SLAM In Dynamic Environment)算法。该算法采用实例分割算法结合相邻帧图像之间的稠密光流对动态物体进...针对目前主流SLAM(同时定位与建图)算法在动态环境中存在精度大幅下降的问题,提出了一种基于光流分割去除动态物体干扰的DY-SLAM(SLAM In Dynamic Environment)算法。该算法采用实例分割算法结合相邻帧图像之间的稠密光流对动态物体进行分割,在SLAM系统图像帧间匹配前剔除动态物体特征点,提高动态环境下的定位精度。使用公开数据集对算法进行评估,算法的RMSE提升最大可达21.59%,能够有效提高系统在复杂动态环境下的定位精度及鲁棒性。展开更多
基金financially supported by the grants for the project "Smart Process Inspection" (funding code ZF4184501CR5) from the "Zentrales Innovationsprogramm Mittelstand" (ZIM)
文摘Particles occur in almost all processes in chemical and life sciences. The particle size and shape influence the process performance and product quality, and in turn they are influenced by the flow behavior of the particles during production. Monitoring and controlling such characteristics in multiphase systems to obtain sufficient qualities will greatly facilitate the achievement of reproducible and defined distributions. So far, obtaining this information inline has been challenging, because existing instruments lack measurement precision, being unable to process overlapping signals from different particle phases in highly concentrated multiphase systems. However, recent advances in photo-optics made it possible to monitor such features(particle size distribution(PSD), aspect ratio and particle concentration) with advanced image analysis(IA) in real-time. New analysis workflows as well as single feature extractions from the images using multiple image analysis algorithms allowed the precise real-time measurements of size, shape and concentration of particle collectives even separated from each other in three phase systems. The performances, advantages and drawbacks with other non-photo-optical methods for assessing the particle size distribution are compared and discussed.
基金supported by the Fundamental Research Funds for the Central Universities under Grant 2020JKF101the Research Funds of Sugon under Grant 2022KY001.
文摘Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms,presenting risks for numerous countries,societies,and individuals,and posing a serious threat to cyberspace security.To address the problem of insufficient extraction of spatial features and the fact that temporal features are not considered in the deepfake video detection,we propose a detection method based on improved CapsNet and temporal–spatial features(iCapsNet–TSF).First,the dynamic routing algorithm of CapsNet is improved using weight initialization and updating.Then,the optical flow algorithm is used to extract interframe temporal features of the videos to form a dataset of temporal–spatial features.Finally,the iCapsNet model is employed to fully learn the temporal–spatial features of facial videos,and the results are fused.Experimental results show that the detection accuracy of iCapsNet–TSF reaches 94.07%,98.83%,and 98.50%on the Celeb-DF,FaceSwap,and Deepfakes datasets,respectively,displaying a better performance than most existing mainstream algorithms.The iCapsNet–TSF method combines the capsule network and the optical flow algorithm,providing a novel strategy for the deepfake detection,which is of great significance to the prevention of deepfake attacks and the preservation of cyberspace security.
文摘针对目前主流SLAM(同时定位与建图)算法在动态环境中存在精度大幅下降的问题,提出了一种基于光流分割去除动态物体干扰的DY-SLAM(SLAM In Dynamic Environment)算法。该算法采用实例分割算法结合相邻帧图像之间的稠密光流对动态物体进行分割,在SLAM系统图像帧间匹配前剔除动态物体特征点,提高动态环境下的定位精度。使用公开数据集对算法进行评估,算法的RMSE提升最大可达21.59%,能够有效提高系统在复杂动态环境下的定位精度及鲁棒性。
