Smart City Gnosys

Smart city article details

Title	A Novel Intelligent Online Fire Monitoring Approach For Underground Pipe Galleries
ID_Doc	3402
Authors	Wang Y.; Wen C.; Zhu Z.; Zhang X.; Song Z.; Zhu Z.; Fan X.
Year	2024
Published	Proceedings of 2024 IEEE 13th Data Driven Control and Learning Systems Conference, DDCLS 2024
DOI	http://dx.doi.org/10.1109/DDCLS61622.2024.10606803
Abstract	Underground pipe galleries are constructed beneath urban areas, providing pathways for the installation of various municipal pipelines. These galleries help protect pipelines and efficiently utilize underground space, serving as a significant facilitator for the construction of smart cities. Due to the confined space inside the galleries and the simultaneous presence of multiple pipelines, there is a potential safety hazard of fire incidents. To ensure the safe operation of pipelines, online monitoring and timely warning of fire incidents are valuable tasks. Current traditional computer vision and machine learning approaches for object detection often experience slow response time. Therefore the You Only Look Once (YOLO) series due to its fast response time, has become a popular method in real-time detection. Nonetheless, YOLOv8 still suffer from information fusion problems. In response to this issue, this paper proposed YOLOH algorithm, which improves YOLOv8 by employing the Gather-and-Distribute (GD) mechanism for intelligent online monitoring of fire incidents in underground pipe galleries. Finally, the effectiveness of YOLOH was validated using a combination of publicly available fire datasets and simulated fire datasets in underground pipe galleries. The YOLOH attains a 63.2 mAP, surpassing the YOLOv8-N baseline by +2.15 mAP. © 2024 IEEE.
Author Keywords	Gather-and-Distribute; Underground pipe galleries; YOLO series; YOLOH

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