## 5 Conclusions  
This study proposes a lightweight, multi-scale detection model, PerMSCA-YOLO, addressing the demands of track surface defect detection in high-speed and heavy-load railways. By incorporating the FasterNet backbone network to reduce computational redundancy and integrating the Multi-Scale Convolutional Attention Module (MSCAM) to enhance the model's ability to detect narrow cracks and minor defects, the model achieves a remarkable balance between accuracy and inference speed. Experimental results demonstrate that PerMSCA-YOLO reaches an [EMAIL_ADDRESS] of 0.856, an F1-score of 0.79, and an inference frame rate of 142 FPS, showcasing its advantages over YOLOv8n and other  
Figure 13: PR curve comparison between different models and [EMAIL_ADDRESS] progression during training. comparable models in terms of detection accuracy and real-time performance. In particular, in the presence of multi-scale defects and complex background interference, PerMSCA-YOLO effectively improves defect recognition capabilities, highlighting its broad potential for deployment in high-speed and heavy-load railway environments. Despite the outstanding performance of PerMSCA-YOLO in terms of accuracy and inference speed, there remain several areas where further optimization is warranted. First, when processing higher-resolution rail images or handling extremely complex scenarios, the inference speed may be affected to some extent. Second, although the use of perceptual loss significantly enhances the detection of subtle defects, its contribution to improving overall accuracy still requires deeper investigation. Moreover, current research places insufficient emphasis on environmental factors such as lighting, reflection, varying capture distances, and train-induced vibrations--factors that can significantly influence model stability and accuracy in high-speed and heavy-load railway detection tasks. Looking ahead, we plan to integrate additional heterogeneous data sources with the PerMSCA-YOLO model to achieve multimodal data fusion. This integration will further expand the model's ability to detect internal rail defects [44] and increase its precision and robustness under complex railway conditions. In addition, we will explore self-supervised pretraining strategies, enabling iterative optimization through large-scale unlabeled or weakly labeled data on top of a small, precisely annotated dataset, thereby reducing reliance on costly labeling processes [45]. With ongoing advances in edge computing, one pressing topic is how to deploy this model on low-power devices while maintaining real-time performance and high accuracy. Future research may also examine the model's adaptability to extreme climate conditions and heavily loaded operational environments, thus providing more comprehensive technical support for railway safety inspections. Meanwhile, by incorporating advanced data augmentation strategies and network structure enhancements, further improvements to transfer learning frameworks can be pursued, thereby boosting the model's resilience in high-speed scenarios and complex defect detection [46]. Overall, despite the sound balance between accuracy and efficiency exhibited by PerMSCA-YOLO in rail surface defect detection, more extensive and diverse application contexts call for additional research. As semi-supervised, self-supervised, and novel deep learning architectures continue to emerge, complemented by more comprehensive datasets and deployment approaches, rail defect detection technology will make even greater strides in safety and reliability, thus contributing to the intelligent operation and maintenance of high-speed and heavy-load railways. Conceptualization, J.Z. and H.Z. ; Methodology, J.Z. ; Software, R.Z. and F.L. ; Validation, H.Z. ; Formal analysis, F.L. ; Investigation, J.Z., R.Z. and H.Z. ; Resources, J.Z. ; Data curation, F.L. ; Writing--original draft, J.Z. and R.Z. ; Writing--review & editing, J.Z., F.L. and H.Z. ; Visualization, R.Z. ; Supervision, H.Z. ; Project administration, H.Z.