# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 2 Related Works
### Image Retrieval Methods  
As shown in Figure 1, searching for target images from a reference library for train fault detection is a typical image retrieval task. Various mature methods are available:  
[PERSON] et al. [25] proposed Deep Supervised Hashing (DSH), using CNNs to learn binary codes for efficient image retrieval with low storage and computational costs, though it struggles with high-resolution images and requires extensive labeled data. [PERSON] et al. [26] introduced an end-to-end learning architecture with R-MAC descriptors and a Siamese network, improving retrieval accuracy but requiring complex training and high-quality labeled datasets. [PERSON] et al. [27] proposed Guided Similarity Separation (GSS) using graph convolutional networks and unsupervised learning to enhance retrieval accuracy, but the method has high computational complexity. [PERSON] et al. [28] introduced a rank loss optimization framework, improving retrieval performance across multiple datasets, although the optimization process is resource-intensive. [PERSON] et al. [29] proposed Content-Based Image Retrieval (CBIR), which relies on extracting visual features like color and texture, but the feature extraction process can be slow for large databases. [PERSON] et al. [30] developed DELF, a deep local feature descriptor effective in handling complex images, though initial training requires significant computational resources. [PERSON] et al. [31] proposed an end-to-end pipeline combining EfficientNet with hybrid Swin-Transformers, achieving state-of-the-art results in large-scale landmark retrieval, though the model's complexity demands substantial computational power and labeled data.  
In summary, numerous advanced technologies and application methods have been proposed in the field of image retrieval, demonstrating significant performance improvements in specific application scenarios. In train fault detection systems, the extremely high resolution and extreme aspect ratio of carriage line-scan images present significant challenges in memory and GPU usage for existing image retrieval methods. While traditional machine learning methods perform well on small-scale datasets, they often fall short on large-scale image data compared to deep learning algorithms. Despite deep learning methods showcasing outstanding performance in image recognition, these models are primarily designed for low-resolution and regular aspect ratio images. Forcibly resizing high-resolution carriage line-scan images to regular sizes would result in the loss of critical detailed features essential for vehicle type recognition.  
Furthermore, accumulating images of different carriage types requires considerable time, and in training-based methods, algorithm accuracy is positively correlated with the number of images collected. When data categories change, the model needs retraining, which is time-consuming and cannot quickly adjust to new target categories. Due to the unique nature of line-scan carriage images, existing methods do not consider these aspects and fail to meet the specific needs of image recognition in train fault detection.  
To address these challenges, this study explores methods for acquiring reference images from the perspective of image classification, aiming to develop a method suitable for quickly obtaining reference images of line-scan carriage images. This approach seeks to reduce dependence on computer hardware configuration and computational resources, improving the efficiency and accuracy of line-scan carriage image recognition.