# An Improved Target Network Model for Rail Surface Defect Detection
## 5 Conclusions  
In this paper, the original YOLOv7 algorithm was improved to propose a more excellent rail surface defect detection algorithm, and MobileNetV3 was used to replace the original backbone network in the feature extraction part. By applying a lightweight feature extraction network, the model size was significantly reduced, leading to potential performance enhancements in detecting rail fasteners. Furthermore, by enhancing the EIoU loss function and utilizing the k-means++ algorithm for data clustering, the instability arising from different initial cluster center selections was mitigated. Compared with the original algorithm, the clustering accuracy is improved, and the algorithm's robustness was enhanced, thus increasing convergence speed and accuracy. Experimental validation has confirmed that the algorithm presented in this paper can achieve rapid and high-precision positioning, yielding a precision of 94.9%, a recall rate of 90.6%, and a mean average precision (mAP) of 95.2%. The detection algorithm exhibits excellent detection accuracy, making it particularly well suited for conducting rail surface defect detection tasks.  
While this paper has made strides in enhancing the mean average precision (mAP), it is important to highlight that the dataset used in this study is from specific railway lines, and further verification is needed to detect surface defects on rails in different environments. Additionally, due to the more detailed feature extraction of MobileNetV3 on the surface condition of rails, the algorithm may mistakenly detect certain areas affected by light and environmental pollution as defects, potentially increasing the false positive rate and requiring further improvement. In future research, we will consider expanding the proposed framework and further improving the lightweight features of the network to optimize the model for application on mobile embedded devices, addressing more complex scenarios in railway surface health analysis.  
Conceptualization, Y.Z. and G.C.; Methodology, T.F.; Resources, Y.S. ([PERSON]); Software, T.F.; Validation, Y.S. ([PERSON]); Writing--original draft, Y.S. ([PERSON]); Writing--review and editing, Y.Z. and G.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Pro-gram of China (\"Research on safety assurance, operation and maintenance technologies of high-speed train based on active-safety\") under Grant 2022 YFB4301204.  
Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **[EMAIL_ADDRESS] (\%)** & **F1** \\ \hline Faster RCNN & 82.1 & 90.1 & 87.4 & 0.86 \\ \hline YOLOv5s & 90.3 & 89.4 & 93.9 & 0.90 \\ \hline YOLOv7 & 84.4 & 90.2 & 90.3 & 0.87 \\ \hline Ours & 94.9 & 90.6 & 95.2 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mainstream target detection algorithm processing effect comparison results.