# FIQ: A Fastener Inspection and Quantization Method Based on Mask FRCN
## 6 Conclusions  
In real railway scenarios, the occurrence of abnormal fasteners is significantly lower than that of normal fasteners. Imbalanced datasets can affect the stability and accuracy of inspection models. Therefore, we need an abnormal fastener quantification method to determine whether a fastener needs to be fixed or not. To address this issue, a lightweight high-precision segmentation network and quantification system for track fasteners is proposed. This method first uses MASK-FRCN to segment the foreground clipping area of the fasteners. After obtaining the segmented connected domains, the state of the track fasteners is quantitatively evaluated by our designed FIQ, which obtains evaluation scores and determines the quality and degree of damage of the track fasteners. Experimental results show that this fastener defect detection method performs well on imbalanced datasets, achieving significant accuracy and speed and has strong robustness. This method can be applied to various types of fastener detection tasks and has important theoretical and practical value.  
This approach effectively reduces false positives and false negatives, improving the accuracy and stability of detection. Our enhanced Mask-FRCN improves segmentation accuracy to 96.01% and reduces network size to 36.1M. FIQ improves the fault detection accuracy of SFC-type fasteners to 95.13%, highlighting our method's efficacy and efficiency.  
The current study, although presenting a novel approach to fastener inspection, acknowledges the need for further optimization to enhance the model's accuracy and processing speed, essential for real-time applications. Moreover, this research is recognized to have simplified the analysis by not extensively addressing the complexities of imbalanced samples. Future work will concentrate on refining the model for real-time detection capa  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Inception Accuracy** \\ \hline SSD & 77.13 \\ YOLO V3 & 83.21 \\ YOLO V5 & 86.41 \\ Our Method & 95.13 \\ \hline \hline \end{tabular}
\end{table}
Table 14: The result of different inception methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SSD** & **YOLO V3** & **YOLO V5** & **Our Method** \\ \hline SSD & 77.13 \\ YOLO V3 & 83.21 \\ YOLO V5 & 86.41 \\ Our Method & 95.13 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of experimental results.  
bilities and expanding the method to include deep learning-based detection of defects in WJ-7 fasteners, thereby tackling the limitations of imbalanced datasets more effectively.  
L.L., Z.S., and S.Z.; data collection: Z.S. and L.L.; analysis and interpretation of results: Z.S. and Y.M.; draft manuscript preparation: Z.S., S.Z., and R.S. All authors reviewed the results and approved the final version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 51975347 and 51907117) and in part by the Shanghai Science and Technology Program (Grant No. 22010501600).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors wish to express sincere appreciation to the reviewers for their valuable comments, which significantly improved this paper. The authors would like to give thanks to the Shanghai University of Engineering Science, for promoting this research and providing the laboratory facility and support.  
The authors declare that they have no conflicts of interest to report regarding the present study.