## 4 Experiments
### Comparative Experiments  
In this section, we evaluate the performance of the PerMSCA-YOLO model against the YOLO series of object detection models and the RT-DETR model under the same dataset and training conditions, in order to validate the proposed model's adaptability, robustness, and overall performance advantages in multi-class rail defect detection tasks. Table 4 summarizes the quantitative results of each detection model in terms of [EMAIL_ADDRESS], F1-score, and FPS. In the table, bold values indicate the best performance among the compared models, and underlined values represent the second-best. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]** & **F1** & **FPS** \\ \hline YOLOv8n & 0.831 & 0.75 (conf = 0.449) & 134 \\ YOLOv9t & 0.824 & 0.76 (conf = 0.575) & 133 \\ YOLOv10n & 0.774 & 0.71 (conf = 0.298) & **187** \\ YOLOv11n & 0.813 & 0.75 (conf = 0.582) & 155 \\ RT-DETR & 0.838 & **0.82 (conf = 0.778)** & 61 \\ PerMSCA-YOLO & **0.856** & 0.79 (conf = 0.462) & 142 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative experimental results. From Table 4, it is evident that YOLOv8n, serving as the baseline model, achieves a [EMAIL_ADDRESS] of 0.831, an F1-score of 0.75, and a speed of 134 FPS. YOLOv9t and YOLOv11n produce similar accuracy, with [EMAIL_ADDRESS] values of 0.824 and 0.813, and F1-scores of 0.76 and 0.75, respectively. By contrast, although YOLOv10n demonstrates superior inference speed, its [EMAIL_ADDRESS] and F1-score drop to 0.774 and 0.71, respectively, indicating that an extreme lightweight design can lead to a noticeable compromise in accuracy. Meanwhile, RT-DETR achieves outstanding detection performance, with a [EMAIL_ADDRESS] of up to 0.838 and  
Figure 10: Comparison of heatmaps between PerMSCA-YOLO and baseline. an F1-score of 0.82; however, it operates at only 61 FPS, meaning its higher accuracy comes at the cost of greater computational overhead. Compared to these models, PerMSCA-YOLO excels in overall performance, attaining a [EMAIL_ADDRESS] of 0.856 and an F1-score of 0.79, while still maintaining 142 FPS. This highlights its ability to balance accuracy and inference speed. Figure 11 presents a comparison of different models' detection outcomes. As shown, PerMSCA-YOLO exhibits stronger robustness and detection accuracy when dealing with complex defects such as micro-cracks and corrosion spots. From the visualized bounding boxes and corresponding confidence scores, PerMSCA-YOLO provides more precise localization and more complete target recognition. In contrast, while YOLOv10n leads in inference speed, it struggles to maintain adequate accuracy on rail surfaces with high texture complexity. RT-DETR is also competitive in terms of precision and recall; however, it is constrained by its relatively low inference speed of 61 FPS, making it less suitable for scenarios that demand high real-time performance. In Figure 12, we present a comparative analysis of heatmaps generated by various models when detecting track defects. These heatmaps visually represent the regions of interest each model focuses on, providing an intuitive view of their response intensities to potential defect areas. It can be observed that PerMSCA-YOLO demonstrates superior accuracy and stronger focus when identifying complex defects. Specifically, the heatmaps for PerMSCA-YOLO illustrate its ability to more precisely concentrate on the finer details of defect regions, distributing its response across multiple scales. This indicates that, through its integration of the multi-scale convolutional attention mechanism and perceptual loss,  
Figure 11: Performance comparison of different models. PerMSCA-YOLO is more adept at maintaining high precision in intricate backgrounds and detecting small targets. By comparison, YOLOv10n, while exhibiting faster inference speeds, appears to generate relatively diffuse and less discriminative heatmaps in high-texture regions, indicating potential difficulty in capturing the full extent of certain subtle defects.