This paper introduces CC-YOLOv9-e, a novel model that incorporates an attention mechanism to boost detection accuracy. The experimental results demonstrate that CC-YOLOv9-e outperforms the baseline YOLOv9-e model across multiple metrics. Specifically, it achieves a mean Average Precision (email protected) of 0.962 and a Recall (R) of 0.942, surpassing other comparative models. This research confirms that the integration of an attention mechanism significantly enhances the detection capabilities of the YOLOv9 model in complex environments, thus offering a more robust solution for campus traffic safety monitoring systems.
Xiao et al. (Mon,) studied this question.