# A Quantitative Detection Method for Surface Cracks on Slab Track Based on Infrared Thermography
## 4 Case Study
### Results Analysis  
To extract the crack features, the thermograms in Figure 14 were processed by the proposed image-processing algorithm, and the results are shown in Figures 15 and 16. It can be observed that through the image enhancement by NSCT and the edge-detection algorithm based on phase congruency, two cracks on the slab surface were effectively identified.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time (min)** & **Internal Temp. (\({}^{\circ}\)C)** & **Temp. in Crack** & **Temp. in Non-Crack** & **Temp. Difference** & **Temp. at** \\  & & **Areas (\({}^{\circ}\)C)** & **Areas (\({}^{\circ}\)C)** & **Areas (\({}^{\circ}\)C)** & **Non-Crack Areas (\({}^{\circ}\)C)** \\ \hline
0 & 20 & 19.6 & 19.6 & 0 & 19.6 \\
50 & 40 & 24.3 & 23.73 & 0.57 & 24.42 \\
75 & 50 & 27.9 & 27.24 & 0.66 & 28.26 \\
100 & 60 & 33.96 & 32.54 & 1.42 & 34.13 \\
115 & 50 & 32.69 & 31.79 & 0.9 & 32.91 \\
130 & 40 & 31.69 & 30.95 & 0.74 & 31.73 \\
145 & 30 & 28.45 & 27.92 & 0.53 & 28.84 \\
160 & 20 & 26.97 & 26.8 & 0.17 & 27.03 \\
175 & 10 & 23.68 & 23.58 & 0.1 & 23.76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The temperature in the crack and non-crack areas and DCJs on the slab surface.  
Figure 14: Infrared thermograms of slab surface of model track at different times.  
Next, the detailed information about the cracks, including the lengths, widths, and areas, was calculated using the proposed quantitative crack-detection method. Since the two cracks were prefabricated with the same length and width, this paper only presents the quantitative detection results of one of them, as listed in Table 5.  
It can be observed from Table 5 that the detection accuracy of the proposed algorithm for the crack width was higher than 98.5%, but the accuracy in the detection of the length and area was only about 85%. The main reason for this problem is that in the infrared thermogram, the surface temperature at the edge of the track slab was higher than that in the middle area, which may have reduced the contrast between the crack and non-crack areas at the edge of the track slab. As a result, it was difficult to detect the cracks on the edge of the slab and the calculated length was shorter than the actual length of the cracks. Nevertheless, according to the current slab-track maintenance rules for HSRs, the level of surface cracking was only determined by its width, with no relevant information on length. Therefore, although the accuracy of the proposed method is limited, to some  
Figure 16: Infrared thermogram of track-slab surface at 130 min and crack-extraction results: (a) infrared thermogram; (b) extracted surface crack; (c) surface crack on infrared thermogram.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time/min**} & **Width** & **Actual** & \multirow{2}{*}{**Acc.**} & **Length** & **Actual** & \multirow{2}{*}{**Acc.**} & **Area** & **Actual** & \multirow{2}{*}{**Acc.**} \\  & **Detected/mm** & & **Width/mm** & & **Detected/mm** & & **Detected/mm\({}^{2}\)** & **Area/mm\({}^{2}\)** & \\ \hline
50 & 1.193 & & 99.4\% & 689 & & 80.4\% & 821.9 & & 80.1\% \\
75 & 1.184 & & 98.7\% & 749 & & 87.4\% & 886.  
degree, for predicting the lengths of slab-surface cracks, it can still meet the actual needs of HSR-slab-track maintenance due to its high performance in the prediction of crack width.