Smart City Gnosys

Smart city article details

Title	Gc-Yolov9: Innovative Smart City Traffic Monitoring Solution
ID_Doc	27740
Authors	An R.; Zhang X.; Sun M.; Wang G.
Year	2024
Published	Alexandria Engineering Journal, 106
DOI	http://dx.doi.org/10.1016/j.aej.2024.07.004
Abstract	In urban smart city environments, traffic hazards can lead to catastrophic outcomes, including significant property losses and severe threats to public safety. Conventional traffic monitoring systems are limited in terms of accuracy and speed, presenting significant challenges for real-time traffic surveillance. To tackle these challenges, this paper introduces the GC-YOLOv9 algorithm. Specifically, we have enhanced the YOLOv9 model by incorporating Ghost Convolution, markedly improving the model's perceptual abilities and detection accuracy. Furthermore, this study designed an integrated smart city framework that includes layers for service applications, the Internet of Things, edge processing, and data centers. By deploying the enhanced YOLOv9 model within this framework, our method achieved mAP@0.5 scores of 77.15 and 74.95 on the BDD100K and Cityscapes datasets, respectively, surpassing existing technologies. Additionally, the potential applications of this method in public area fire safety management, forest fire monitoring, and intelligent security systems further underscore its significant value in improving the safety and efficiency of smart cities.
Author Keywords	Ghost convolution; Internet of Things; Real-time data processing; Smart cities; Traffic monitoring; YOLOv9

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