# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 4 Experiments
### Image Recognition Accuracy Evaluation  
Due to different movement modes, the same carriage can produce four different types of line-scan integrated images. Since the structure of the carriage is asymmetrical, images captured from the left side differ in features from those captured on the right side. Images captured from the same side but with different movement directions will appear as left-right flipped versions of each other. Spatially, these four subcategories of images have significant differences; however, from an image feature perspective, they exhibit high similarity, which directly affects image recognition accuracy. Although the image category relationships may seem straightforward, they pose significant challenges for existing image recognition algorithms. To fully demonstrate these challenges and highlight the advantages of our proposed method, we reconstructed a dataset with 19 categories based on the original carriage dataset, ignoring distinctions in carriage movement modes. Comparing the accuracy of algorithms on these two datasets allows us to evaluate their ability to recognize different carriage models and their effectiveness in handling images from various movement modes. Given that our method achieves 100% recognition accuracy across all categories, we present the average Top-1 Accuracy of subcategories for each model category to save space. Table 2 presents the accuracy comparison between our method and the Bag-of-Words (BoW) image recognition method. The BoW model combines five feature extraction methods to construct visual word representations of images: AKAZE [34], BRISK [35], ORB [36], SIFT [37], and SuperPoint [38], a deep learning-based feature extraction method known for its superior local feature representation capabilities compared to traditional methods. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\Sigma\) & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** & **1.0** \\ \hline Mean Recognition & & & & & & & & & & \\ Accuracy (\%) & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 87.50 \\ \hline Mean Matching Ratio (\%) & 99.77 & 99.84 & 99.71 & 99.96 & 100.00 & 97.46 & 96.86 & 96.66 & 96.25 & 95.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Impact of \(\Sigma\) on mean recognition accuracy (%) and mean matching ratio (%). These five feature extraction methods are commonly used in computer vision and are representative of the field. From Table 2, it is evident that the representation ability of feature descriptors directly determines the classification accuracy of the BoW model--the stronger the feature representation, the higher the image recognition accuracy. The deep learning-based feature extraction method achieves the best recognition accuracy in the BoW model. However, the BoW model, which relies on local features, is sensitive to local feature differences in images. For carriage types with inconsistent images due to complex usage environments, the recognition accuracy is poor (e.g., B23, BX1K, BX1K-1, NX17K, and X70). For categories with high image consistency, the recognition accuracy approaches 100% (e.g., BDL1, C70, NX70A, and C64K). To further evaluate the impact of local nonlinear distortions on traditional image recognition methods, we conducted a comparative experiment using the template matching algorithm based on normalized cross-correlation, as described in [PERSON] and [PERSON] [39]. This method is commonly employed in image processing tasks for template matching by calculating the correlation between image regions. In our experiment, we applied this method to the 79-class carriage linear array image classification task. The results demonstrate that the normalized cross-correlation method performs poorly on this task, with a significant decline in recognition accuracy.