Flood detection by using FCN-AlexNet

Son, Keumyoung; Yıldırım, Mustafa Eren; Park, Jang-Sik; Song, Jong-Kwan

doi:10.1117/12.2523028

Cited by 4 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of layers that associate with learnable weights, AlexNet consists of a total of eight layers, including five convolutional layers and three fully connected layers [24]. e structure of AlexNet is illustrated in Figure 3.…”

Section: Alexnetmentioning

confidence: 99%

Fingerspelling Identification for Chinese Sign Language via AlexNet-Based Transfer Learning and Adam Optimizer

Jiang

Satapathy

et al. 2020

Scientific Programming

View full text Add to dashboard Cite

As an important component of universal sign language and the basis of other sign language learning, finger sign language is of great significance. This paper proposed a novel fingerspelling identification method for Chinese Sign Language via AlexNet-based transfer learning and Adam optimizer, which tested four different configurations of transfer learning. Besides, in the experiment, Adam algorithm was compared with stochastic gradient descent with momentum (SGDM) and root mean square propagation (RMSProp) algorithms, and comparison of using data augmentation (DA) against not using DA was executed to pursue higher performance. Finally, the best accuracy of 91.48% and average accuracy of 89.48 ± 1.16% were yielded by configuration M1 (replacing the last FCL8) with Adam algorithm and using 181x DA, which indicates that our method can identify Chinese finger sign language effectively and stably. Meanwhile, the proposed method is superior to other five state-of-the-art approaches.

show abstract

Section: Alexnetmentioning

confidence: 99%

Fingerspelling Identification for Chinese Sign Language via AlexNet-Based Transfer Learning and Adam Optimizer

Jiang

Satapathy

et al. 2020

Scientific Programming

View full text Add to dashboard Cite

show abstract

Deep Sensing of Urban Waterlogging

Chang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In the monsoon season, sudden flood events occur frequently in urban areas, which hamper the social and economic activities and may threaten the infrastructure and lives. The use of an efficient largescale waterlogging sensing and information system can provide valuable near real-time disaster information to facilitate disaster management and enhance awareness of the general public to alleviate losses during and after flood disasters. Therefore, in this study, a visual sensing approach driven by deep neural networks and information and communication technology was developed to provide an end-to-end mechanism to realize waterlogging sensing and event-location mapping. The use of a deep sensing system in the monsoon season in Taiwan was demonstrated, and waterlogging events were predicted on the island-wide scale. The system could sense approximately 2379 vision sources through an internet of video things framework and transmit the event-location information in 5 min. The proposed approach can sense waterlogging events at a national scale and provide an efficient and highly scalable alternative to conventional waterlogging sensing methods.INDEX TERMS deep neural network, internet of video things, urban flood, urban waterlogging, visual sensing

show abstract

The Use of Artificial Intelligence in Disaster Management - A Systematic Literature Review

Goodwin

2019

2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

View full text Add to dashboard Cite

Flood detection by using FCN-AlexNet

Cited by 4 publications

References 7 publications

Fingerspelling Identification for Chinese Sign Language via AlexNet-Based Transfer Learning and Adam Optimizer

Fingerspelling Identification for Chinese Sign Language via AlexNet-Based Transfer Learning and Adam Optimizer

Deep Sensing of Urban Waterlogging

The Use of Artificial Intelligence in Disaster Management - A Systematic Literature Review

Contact Info

Product

Resources

About