# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 4 Experiments
### Image Recognition Accuracy Evaluation
Specifically, the method fails to effectively handle the local nonlinear scale distortions present in the carriage linear array images, leading to a substantial reduction in recognition accuracy, and in many cases, the method fails to produce valid matches altogether. The poor performance is attributed to the sensitivity of correlation-based methods to image distortions and inconsistencies. In particular, the height inconsistency between carriage images and the scale distortions cause significant changes in the image features, making it difficult for the normalized cross-correlation to produce reliable results. This \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Class**} & \multicolumn{8}{c}{**76-Category**} & \multicolumn{8}{c}{**19-Category**} \\ \cline{2-13}  & **AKAZE** & **BRISK** & **ORB** & **SIFT** & **Super** & **NCC** & **Ours** & **AKAZE** & **BRISK** & **ORB** & **SIFT** & **Super** & **Ours** \\  & [34] & [35] & [36] & [37] & [38] & [39] & **Ours** & **[34]** & **[35]** & **[36]** & **[37]** & **[38]** & **Ours** \\ \hline B22 & 0.087 & 0.095 & 0.11 & 0.13 & 0.122 & 0.059 & 1.0 & 0.133 & 0.255 & 0.225 & 0.69 & 0.355 & 1.0 \\ \hline B22-1 & 0.397 & 0.225 & 0.23 & 0.27 & 0.32 & 0.238 & 1.0 & 0.488 & 0.278 & 0.183 & 0.71 & 0.773 & 1.0 \\ \hline B23 & 0.14 & 0.167 & 0.112 & 0.1275 & 0.17 & 0.093 & 1.0 & 0.44 & 0.518 & 0.365 & 0.227 & 0.304 & 1.0 \\ \hline B23-1 & 0.445 & 0.502 & 0.477 & 0.455 & 0.477 & 0.421 & 1.0 & 0.688 & 0.82 & 0.988 & 0.30 & 0.361 & 1.0 \\ \hline BDL1 & 1.0 & 1.0 & 0.975 & 0.998 & 0.985 & 0.942 & 1.0 & 1.0 & 0.715 & 0.59 & 0.295 & 0.613 & 1.0 \\ \hline BH1 & 0.16 & 0.09 & 0.057 & 0.237 & 0.45 & 0.149 & 1.0 & 0.308 & 0.113 & 0.14 & 0.41 & 0.19 & 1.0 \\ \hline BX1K & 0.112 & 0.109 & 0.108 & 0.152 & 0.252 & 0.097 & 1.0 & 0.444 & 0.438 & 0.385 & 0.644 & 1.0 & 1.0 \\ \hline BX1K-1 & 0.082 & 0.129 & 0.102 & 0.087 & 0.112 & 0.052 & 1.0 & 0.179 & 0.185 & 0.185 & 0.35 & 0.448 & 1.0 \\ \hline C64K & 1.0 & 0.982 & 1.0 & 1.0 & 0.93 & 0.932 & 1.0 & 1.0 & 0.63 & 0.565 & 0.69 & 0.68 & 1.0 \\ \hline C70E & 0.243 & 0.043 & 0.058 & 0.23 & 0.02 & 0.069 & 1.0 & 0.8 & 1.0 & 1.0 & 0.68 & 0.57 & 1.0 \\ \hline C70 & 1.0 & 0.968 & 1.0 & 0.998 & 0.995 & 0.942 & 1.0 & 1.0 & 0.578 & 0.595 & 0.843 & 0.503 & 1.0 \\ \hline JSQ5 & 0.273 & 0.175 & 0.168 & 0.188 & 0.335 & 0.178 & 1.0 & 0.388 & 0.315 & 0.401 & 0.688 & 0.995 & 1.0 \\ \hline JSQ6 & 0.089 & 0.164 & 0.196 & 0.199 & 0.139 & 0.107 & 1.0 & 0.181 & 0.544 & 0.628 & 0.818 & 0.74 & 1.0 \\ \hline JSQ6-1 & 0.163 & 0.155 & 0.143 & 0.155 & 0.203 & 0.114 & 1.0 & 0.315 & 0.3325 & 0.45 & 0.2525 & 0.435 & 1.0 \\ \hline NX17K & 0.133 & 0.233 & 0.165 & 0.118 & 0.118 & 0.103 & 1.0 & 0.14 & 0.388 & 0.36 & 0.308 & 0.463 & 1.0 \\ \hline NX70A & 0.998 & 0.995 & 0.963 & 0.998 & 0.995 & 0.940 & 1.0 & 1.0 & 1.0 & 0.963 & 0.998 & 0.593 & 1.0 \\ \hline NX70 & 0.515 & 0.503 & 0.548 & 0.470 & 0.483 & 0.454 & 1.0 & 0.53 & 0.683 & 0.538 & 0.373 & 0.123 & 1.0 \\ \hline X68 BK & 0.5 & 0.573 & 0.543 & 0.455 & 0.473 & 0.459 & 1.0 & 0.728 & 0.363 & 0.18 & 0.413 & 0.303 & 1.0 \\ \hline X70 & 0.1 & 0.113 & 0.143 & 0.280 & 0.018 & 0.081 & 1.0 & 0.16 & 0.793 & 0.668 & 0.605 & 1.0 & 1.0 \\ \hline Ave. ACC & 0.391 & 0.379 & 0.373 & 0.397 & 0.399 & 0.338 & 1.0 & 0.515 & 0.51 & 0.481 & 0.538 & 0.549 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of recognition accuracy: proposed method vs. Bag-of-Words method. further highlights the challenges posed by carriage linear array images and demonstrates the limitations of traditional image recognition methods when dealing with images subject to such distortions. Comparing the classification accuracy of the two datasets in Table 2, the 19-category dataset performs better than the 76-category one. The total number of images in both datasets is the same, but the reduced number of categories increases the diversity of images within each category.