We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge-to learn damaged objects in aerial videos.Ameta-learning system was integrated with the fuzzy broad learni...We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge-to learn damaged objects in aerial videos.Ameta-learning system was integrated with the fuzzy broad learning system to further develop the theory of federated learning.Both the mixed picture set of aerial video segmentation and the 3D-reconstructed mixed-reality data were employed in the performance of the broad federated meta-learning system.The study results indicated that the object classification accuracy is up to 90%and the average time cost in damage detection is only 0.277 s.Consequently,the broad federated meta-learning system is efficient and effective in detecting damaged objects in aerial videos.展开更多
基金This research was funded by the Strategic Priority Research Program of Chinese Academy of Sciences(XDA20060303)the National Natural Science Foundation of China(41571299)the High-Level Base-Building Project for Industrial Technology Innovation(1021GN204005-A06).
文摘We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge-to learn damaged objects in aerial videos.Ameta-learning system was integrated with the fuzzy broad learning system to further develop the theory of federated learning.Both the mixed picture set of aerial video segmentation and the 3D-reconstructed mixed-reality data were employed in the performance of the broad federated meta-learning system.The study results indicated that the object classification accuracy is up to 90%and the average time cost in damage detection is only 0.277 s.Consequently,the broad federated meta-learning system is efficient and effective in detecting damaged objects in aerial videos.
