# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 6 Conclusions  
In this paper, the rail corrugation identification and assessment issues are concerned. Firstly, the improved SPM model for rail corrugation identification is introduced, in which the multi-features (including Uniform LBP and dense_SIFT) and locality-constrained linear coding are used. The quantitative experimental results indicate that the proposed identification method achieves markedly better performance. Specifically, the precision and recall of the proposed corrugation identification method are 99.67% and 98.34%. After that, to further analysis the corrugation, an innovative estimation method for rail corrugation period based on one-dimensional fast Fourier transform and normalized distance matching function is proposed. The experimental results demonstrate that the proposed method can be applied to estimate the rail corrugation period efficiently. Finally, the wear saliency (WS) based on the visual significance analysis of the corrugation image is calculated to assess the rail corrugation severity. To the best knowledge of the authors, this method is the first one that introduces the concept of wear saliency (WS) to assess the severity of the rail corrugation in terms of the human visual system.  
In the future, our research work will mainly focus on the following aspects. On the one hand, the investigation and construction of an intelligent visual inspection system for rail corrugation, including an image acquisition subsystem installed at the bottom of the operating vehicle and a background corrugation identification and analysis system. The methods proposed in this paper will be further validated and the relevant results will be published. On the other hand, other different types of signals  
Figure 15: The visualization process and severity assessment result. (**a**) the original corrugation image; (**b**) the result rustless corrugation image after using horizontal gray gradient statistical (HGGS); (**c**) the corrugation saliency map obtained by normalized frequency tuned (NFT); (**d**) the result of image binarization; (**e**) the result of each corrugation localization and the wear saliency (WS) of them; and (**f**) the final severity assessment result of each corrugation area.  
(e.g., vibration acceleration and sound) will be carefully investigated and combined in the future researches to assess the severity of the rail corrugation more accurately.  
[PERSON] collected and analyzed the data, made charts and diagrams, conceived and performed the experiments and wrote the paper; [PERSON] conceived the structure and provided guidance; [PERSON] collected and processed the data; [PERSON] modified the manuscript; [PERSON] arranged the rail image acquisition time and line.  
This work is partly supported by Chinese National Key Project of Research and Development (Contract No. 2016 YFB1200402).  
The authors declare no conflict of interest.  
The rail surface localization algorithm used in this paper is shown in Algorithm A1, of which the detailed procedure is discussed in Section 3.2.  
```
1:The enhanced rail image \(enImg\); The rail width \(w_{r}\);
2:The starting position of the rail \(p_{r}\);
3:procedureAlgorithm 1(\(enImg,w_{r}\))
4:\(h\gets size(enImg,1)\)
5:\(w\gets size(enImg,2)\)
6:for\(i\gets 2,w-1\)do
7:\(AW(i)\gets adaptive\) weight of \(i\)-th column
8:\(G(i)\gets enImage(\cdot,i+1)-enImage(\cdot,i)\)
9:\(SG(i)\gets sum\) of \(G(i)\)
10:\(WG(i)\gets SG(i)*AW(i)\)
11:endfor
12:\(maxValue\gets 0\)
13:\(p_{r}\gets 1\)
14:for\(i\gets 1,w-w_{r}+1\)do
15:\(interVal\gets WG(i:i+w_{r}-1)\)
16:\(sInterval\gets sum\) of interVal
17:if\(sInterval>maxValue\)then
18:\(maxValue\gets sInterVal\)
19:\(p_{r}\gets i\)
20:endif
21:endfor
22:return\(p_{r}\)
23:endprocedure
```  
**Algorithm A1** The Algorithm for rail surface localization.