GC-YOLOv9: Innovative smart city traffic monitoring solution

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...

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Vydáno v:Alexandria engineering journal Ročník 106; s. 277 - 287
Hlavní autoři: An, Ru, Zhang, Xiaochun, Sun, Maopeng, Wang, Gang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.11.2024
Elsevier
Témata:
Ghost convolution
Internet of Things
Real-time data processing
Smart cities
Traffic monitoring
YOLOv9
YOLOv9
Smart cities
Real-time data processing
Internet of Things
Traffic monitoring
Ghost convolution
ISSN:1110-0168
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Popis
Shrnutí: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.
ISSN:1110-0168
DOI:10.1016/j.aej.2024.07.004