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Smart city article details
| Title | Cloud-Based Image Segmentation Approach For Internet Of Drones (Iod) |
|---|---|
| ID_Doc | 14461 |
| Authors | Gupta B.B.; Gaurav A.; Chui K.T.; Arya V.; Wu J. |
| Year | 2024 |
| Published | IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2024 |
| DOI | http://dx.doi.org/10.1109/INFOCOMWKSHPS61880.2024.10620854 |
| Abstract | We propose a novel cloud-based image segmentation framework tailored for the Internet of Drones (IoD), leveraging advanced neural network architectures to process aerial imagery. Our approach utilizes a modified U-Net model, implemented at a cloud-enabled Base Station, to ensure efficient, scalable image analysis without burdening drone resources. The system capitalizes on high-speed 5G connectivity for real-time data transmission, achieving significant improvements in segmentation accuracy as demonstrated on a diverse 24-class dataset. Our results indicate a promising enhancement in computational efficiency and a reduction in drone energy consumption. This research contributes to IoD applications in smart city planning, agriculture, and surveillance by optimizing the synergy between drone technology and cloud computing capabilities. © 2024 IEEE. |
| Author Keywords | Cloud Computing; Deep Learning; Image Segmentation; Internet of Drones (IoD); Neural Networks; U-Net Architecture |
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