@Article{cmes.2023.028670,
AUTHOR = {Zekai Li, Wenfeng Wang},
TITLE = {Broad Federated Meta-Learning of Damaged Objects in Aerial Videos},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {3},
PAGES = {2881--2899},
URL = {http://www.techscience.com/CMES/v137n3/53728},
ISSN = {1526-1506},
ABSTRACT = {We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge— to
learn damaged objects in aerial videos. A meta-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.},
DOI = {10.32604/cmes.2023.028670}
}