# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 4 Experiments  
Freight train carriages are designed and manufactured to meet the diverse requirements of different types of cargo. Due to the wide variety of rail freight types, there is also a rich variety of corresponding carriage models. Additionally, the complex outdoor  
Figure 5: Comparison of point sets and their GMM probability densities before and after RBF transformation with optimized weights. (**a,d**) show the intra-category transformation within 1-LRAB. (**b,e**) highlight the transformation from 1-LRBA to 1-LRAB. (**c,f**) present the transformation from 1-RLAB to 1-LRAB.  
environment on railway lines means that the consistency of line-scan images of freight train carriages is far inferior to that of subway and high-speed train carriages. Therefore, using line-scan images of freight train carriages to evaluate the performance of image recognition algorithms is more convincing. In this experiment, we collected line-scan images of 19 different models of freight carriages. Based on the different movement directions of the vehicles, the images for each model were further subdivided into four subcategories: LRAB, RLBA, RLAB, and RLBA. Each subcategory contains 200 different images, exhibiting varying degrees of local nonlinear scale distortion in the horizontal direction. This results in a dataset comprising 76 categories and a total of 1.52 million high-resolution carriage images. Each image has a height resolution of 2048 pixels, and the width resolution ranges from n \(\times\) 2000 (where n = 8 to 13), depending on the length of the carriage. The dataset is evenly divided into a training set and a testing set. Additionally, to evaluate our method, we constructed a standard reference image library consisting of 76 undistorted standard images, with one reference image for each category. Object detection was performed on both the test set and the template set, and the results were converted into corresponding point sets for subsequent algorithm performance evaluation.  
In Section 2, we analyzed and discussed existing image classification methods. Among machine learning-based image recognition algorithms, the Bag-of-Words (BoW) model stands out for its high computational efficiency and scalability, particularly in handling large-scale feature descriptors. Given the high resolution of line-scan carriage images, the extracted local feature quantities are substantial. Considering processing speed and algorithmic complexity, other traditional machine learning methods are unsuitable for recognizing line-scan carriage images. Therefore, in our experiments, we selected the BoW model as a representative machine learning method, combined with different feature extraction methods, to compare recognition performance. Additionally, the unique nature of line-scan carriage images requires substantial memory and GPU resources for existing image retrieval methods. Currently, we do not have the hardware capabilities to conduct comparative experiments under these conditions. Ultimately, we selected three categories of methods for comparison: BoW-based methods, deep learning-based multi-class image classification methods, and the proposed method. Performance evaluation on the carriage dataset was conducted in terms of image recognition accuracy, processing speed, and algorithm robustness.  
During the evaluation of deep learning-based methods, high-resolution input images caused GPU memory shortages, hindering model training and inference. Conversely, excessively low resolutions resulted in significant loss of image details, affecting recognition accuracy. To balance GPU memory consumption and accuracy loss, we uniformly scaled all network model input images to a resolution of \(448\times 3200\) pixels. In other comparative experiments, we downsampled the input images by a factor of 4 to ensure a resolution similar to that of the multi-classification network model, thereby ensuring the fairness and rigor of the comparative experiments.