Smart City Gnosys
Smart city article details
| Title | Smokeaway: An Early Smoke Detection Iot Architecture For Outdoors |
|---|---|
| ID_Doc | 51920 |
| Authors | Alqarafi L.; Aloufi N.; Alluqmani R.; Alrehaili S.; Alotaibi M. |
| Year | 2023 |
| Published | 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2023 |
| DOI | http://dx.doi.org/10.1109/GCAIoT61060.2023.10385117 |
| Abstract | The lack of available smoke detectors outdoors constitutes a significant factor contributing to the rapid spread of fires before effective containment can be achieved. The integration of outdoor smoke detectors helps tremendously with reducing fire and smoke hazards, consequently fostering a safe environment. With the emergence of smart cities and the revolutionary shift in relying on technologies such as the Internet of Things (IoT) and artificial intelligence (AI) tools, the current stage requires developing systems that integrate these tools and leverage them. To address that, this paper presents SmokeAway, a reliable system to promptly and accurately detect outdoor smoke. In order to do so, we propose a layered IoT architecture that relies on computer vision and a deep learning model. The efficacy of our model is assessed through two key performance metrics: accuracy and categorical cross-entropy loss. Compared to other models, our model has achieved notably low loss values, and accuracy rates of up to 96%, outperforming alternate models. © 2023 IEEE. |
| Author Keywords | Deep Learning; Image Processing; Internet of Things; Transfer Learning |
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