## 4 Experiments
### Ablation Experiments  
To further investigate the contribution of each module in the PerMSCA-YOLO model and the synergistic effects within its overall performance, a series of ablation experiments were conducted. By incrementally incorporating each module into the YOLOv8n model and performing comparative analyses, we gained a deeper understanding of how each module independently and collaboratively enhances the model's accuracy, inference efficiency, and robustness. Using the YOLOv8n model as the baseline, we evaluated the impact of each modification. Table 3 illustrates that the introduction of each module results in significant variations in various performance metrics, providing strong evidence for the rationality and effectiveness of the model's design. In the table, bold values indicate the best performance among the compared models, and underlined values represent the second-best. After incorporating the FasterNet backbone, the model's accuracy slightly decreased, with the mAP dropping from 0.831 to 0.822. This indicates that while the FasterNet backbone optimized the computational efficiency of the model, it slightly compromised detection accuracy. However, this reduction was marginal, and the inference frame rate improved significantly from 134 FPS to 185 FPS, demonstrating FasterNet's substantial contribution to enhancing inference speed. This improvement highlights FasterNet's advantage in minimizing computational redundancy while maintaining a high inference \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**FasterNet**} & \multirow{2}{*}{**MSCAM**} & \multicolumn{2}{c}{**Perceptual**} & \multirow{2}{*}{**[EMAIL_ADDRESS]**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**FPS**} \\  & & & & & 0.831 & 0.75 (conf = 0.449) & 134 \\  & \(\surd\) & & & 0.822 & 0.78 (conf = 0.596) & **185** \\  & & \(\surd\) & & 0.847 & **0.80 (conf = 0.556)** & 112 \\ YOLOv8n & & & & \(\surd\) & 0.842 & 0.77 (conf = 0.660) & 135 \\  & \(\surd\) & \(\surd\) & & 0.839 & 0.76 (conf = 0.532) & 164 \\  & \(\surd\) & & \(\surd\) & 0.834 & 0.75 (conf = 0.517) & 181 \\  & & \(\surd\) & \(\surd\) & **0.861** & 0.79 (conf = 0.487) & 109 \\  & \(\surd\) & \(\surd\) & \(\surd\) & 0.856 & 0.79 (conf = 0.462) & 142 \\ \hline \hline \end{tabular} Note: Bold values indicate the best performance among all models, underlined values represent the second-best, and \(\surd\) denotes the use of the corresponding module in the model.  
\end{table} 
Table 3: Ablation experiment results. speed, which is particularly significant for resource-constrained applications in track defect detection tasks. Following the introduction of the MSCAM module, mAP increased to 0.847, signifying that MSCAM effectively enhanced the model's ability to perceive multi-scale defects. By utilizing channel attention, spatial attention, and multi-scale convolutions in a cascading mechanism, MSCAM improved the model's capability to recognize complex defects such as narrow cracks and corrosion patches (Figure 9's heatmap corroborates this). However, due to the multi-branch convolutions and channel shuffling operations involved in MSCAM, the inference speed decreased, with FPS dropping to 112. This result underscores the trade-off between increased detection accuracy and the computational overhead introduced by MSCAM. With the addition of perceptual loss, both [EMAIL_ADDRESS] and F1-score improved to 0.842 and 0.77, respectively, and the optimal F1-score confidence threshold increased to 0.660. Perceptual loss aligns predicted and ground-truth bounding boxes in the deep feature space, strengthening the model's focus on defect textures (such as the continuity of crack edges) and reducing false positives caused by surface rust or oil stains. Although the  
Figure 9: Test results of the PerMSCA-YOLO model. From these test results, it can be observed that the PerMSCA-YOLO model is capable of effectively detecting various types of rail damage.