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In modern urban environments, efficient traffic management is a critical component of smart cities. The increasing complexity of traffic systems necessitates advancements in real-time monitoring technologies. Among these, the YOLO series of algorithms, specifically the GC-YOLOv9, stands out for its enhanced performance in object detection. This article delves into the innovations in traffic monitoring algorithms, particularly focusing on the GC-YOLOv9 model. The GC-YOLOv9 introduces ghost convolution technology, significantly improving the model’s accuracy and efficiency. Additionally, this paper discusses the integration of IoT frameworks with edge computing to enable real-time data processing, further enhancing smart city infrastructure.

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Developments in Smart City Traffic Monitoring Algorithms with a Focus on GC-YOLOv9

  • 会议论文

  • 发表日期：

    2025年

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  • Lahcen Gouskir,
  • Mohamed Baslam,
  • Mohamed Gouskir,
  • Abdelmoula Abouhilal,
  • Soufiane Belhouideg,
  • Hanaa Hachimi

摘要

In modern urban environments, efficient traffic management is a critical component of smart cities. The increasing complexity of traffic systems necessitates advancements in real-time monitoring technologies. Among these, the YOLO series of algorithms, specifically the GC-YOLOv9, stands out for its enhanced performance in object detection. This article delves into the innovations in traffic monitoring algorithms, particularly focusing on the GC-YOLOv9 model. The GC-YOLOv9 introduces ghost convolution technology, significantly improving the model’s accuracy and efficiency. Additionally, this paper discusses the integration of IoT frameworks with edge computing to enable real-time data processing, further enhancing smart city infrastructure.