In recent years, there have been frequent incidents of foreign objects intruding into railway and Airport runways. These objects can include pedestrians, vehicles, animals, and debris. This paper introduces an improved YOLOv5 architecture incorporating FasterNet and attention mechanisms to enhance the detection of foreign objects on railways and Airport runways. This study proposes a new dataset, the aero and rail foreign object detection (AARFOD), which combines two public datasets for detecting foreign objects in aviation and railway systems. The dataset aims to improve the recognition capabilities of foreign object targets. Experimental results on this large dataset have demonstrated significant performance improvements of the proposed model over the baseline YOLOv5 model, reducing computational requirements. Improved YOLO model shows a significant improvement in precision by $1.2 \%$, recall rate by $1.0 \%$, and mAP@. 5 by $0.6 \%$, while mAP@. $5-.95$ remained unchanged. The parameters were reduced by approximately ${2 5. 1 2 \%}$, and GFLOPs were reduced by about $10.63 \%$. In the ablation experiment, it is found that the FasterNet module can significantly reduce the number of parameters of the model, and the reference of the attention mechanism can slow down the performance loss caused by lightweight.
