# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 1 Introduction  
With the continuous expansion of railway networks and the increase in train speeds, the importance of train body fault detection technology has become increasingly prominent. Failure to accurately identify train faults in a timely manner poses serious safety risks and can increase operating costs. Therefore, researchers have designed systems such as the Trouble of Moving EMU Detection System (TEDS), Trouble of Moving Freight Detection System (TFDS), and Trouble of Moving Vehicle Detection System (TVDS) to monitor train fault conditions [1; 2; 3; 4]. These systems use multiple cameras installed around the tracks to capture images of the train car bodies from different angles. Figure 1 shows a train carriage line-scan image acquisition system. When the train passes through the camera's field of view, the cameras capture images of the carriage surface from various observation angles. By extracting standard reference images of the same category from a pre-established reference image library and performing comparative analysis, these systems can evaluate the train's fault status. Figure 1 illustrates the fault detection process; for more details, please refer to our previous work [3]. The first step in this process is to accurately select the reference images for comparison, which is crucial for the accuracy of subsequent fault analysis. Ensuring the correct selection of reference images depends on accurately identifying the category of the current image. Thus, accurate identification of carriage image categories is essential in the train fault detection system. Currently, in train image acquisition systems, image category identification is primarily achieved by recognizing the carriage number information using specialized equipment while capturing the line-scan images of the carriage. This information includes the model and a unique identifier of the carriage, from which the model information can be parsed to label the category of the current image. Existing carriage number recognition equipment can be classified into two types based on their working principles. One type uses microwave communication technology. When the train passes between microwave antennas installed on the tracks, the equipment reads the Radio Frequency (RF) tags installed at the bottom of the carriages, which store the carriage number information, as shown in Figure 2a. The other type uses visual character recognition technology to directly recognize the carriage number characters painted on the side of the carriage, as shown in Figure 2b. Figure 1: Train fault detection system. (**a**) Train line-scan image acquisition system; (**b**) the reference image library contains standard images of all carriage types without scale distortion, each annotated with key component information for subsequent fault analysis; (**c**) acquired line-scan images may have nonlinear scale distortions in the horizontal direction; (**d**) correspondence between horizontal pixel coordinates in (**c**) and standard reference image coordinates; (**e**) main processing steps in train fault detection. Figure 2: Carriage identification equipment. (**a**) Carriage identification equipment based on microwave communication; (**b**) carriage identification equipment based on visual character recognition. However, the real-world operating conditions of trains are complex, especially for freight trains, where strong vibrations and unpredictable environmental factors can cause the pre-installed RFID tags to be lost or damaged. In such cases, the microwave communication-based carriage number recognition equipment cannot mark the collected image categories, rendering the fault detection system inoperative. Additionally, the carriage number markings can be worn out or obscured by dust, leading to blurred characters in the images, as shown in Figure 2b. This compromises the accuracy of character recognition-based carriage number identification. Furthermore, the high cost of AEI equipment significantly increases the cost of the fault detection system.