## 2 Related Work  
### Traditional Methods  
In the early stages of railway defect detection, the process predominantly relied on manual inspection and basic non-destructive testing (NDT) techniques for periodic track evaluation. Manual inspections were typically conducted by maintenance personnel using visual observation or simple mechanical tools to inspect track surfaces. While intuitive, this method is inefficient and heavily influenced by subjective factors, making it unsuitable for long-distance or high-intensity track inspection scenarios. Consequently, researchers have explored various traditional NDT approaches to enhance automation and detection accuracy, with notable techniques including infrared thermography, eddy current testing, ultrasonic testing, and electromagnetic testing. Infrared thermography detects defect locations and sizes based on the temperature distribution of the target surface, typically using passive or active heating methods. [PERSON] et al. [8] noted that IRT has shown promising results in layer-wise detection of buildings and infrastructure and has also provided valuable insights for detecting track surface delamination and cracks. Its advantages include large-area real-time coverage and non-contact detection. However, when defects are deep or temperature changes are minimal, signals are often affected by environmental factors such as humidity, wind speed, and ambient temperature, leading to unstable detection results. Eddy current testing is an electromagnetic method that induces eddy currents on a metal surface using alternating current and identifies defects by measuring impedance changes in a sensor coil. [PERSON] et al. [9] developed an embedded system-based track defect detection and classification method using eddy currents, combining continuous wavelet transform with convolutional neural networks (CNNs) to automatically identify and classify welding defects, pits, and joints. [PERSON] et al. [10] further proposed a multi-channel eddy current detection algorithm that generates 2D and 3D defect images in real time, supporting field-based quantitative assessments. However, ECT is prone to signal distortion when applied to complex curved surfaces or high-speed testing, necessitating optimized sensor arrangements and data post-processing techniques to improve robustness. Ultrasonic testing primarily detects internal or surface defects such as cracks and inclusions by emitting and receiving short pulse waves. Traditional contact-based ultrasonic testing requires the application of a coupling agent to the track surface, making it unsuitable for rapid or large-scale inspections. [PERSON] et al. [11] highlighted the advantages of ultrasonic guided wave (UGW) technology for detecting surface cracks in metal structures, noting its remote diagnostic capabilities and adaptability to complex structures, which is beneficial in fields such as railways and pipelines. [PERSON] et al. [12] studied a laser ultrasonic-based track surface defect detection model, demonstrating the superior performance of high-frequency ultrasound in detecting small-scale cracks. Nonetheless, ultrasonic testing faces challenges in terms of coupling effectiveness, sensor placement, and detection speed when implemented on high-speed trains or large-scale tracks. In addition to eddy current and ultrasonic methods, electromagnetic testing includes techniques such as direct current electromagnetic methods and flux leakage. [PERSON] et al. [13] utilized the interaction between DC magnetic fields and motion-induced eddy currents to quantify the depth of track cracks in high-speed electromagnetic NDT, using numerical simulations to determine optimal detection positions and enhance sensitivity. Similarly, [PERSON] et al. [14] and other researchers often combine mechanical and electromagnetic models in systematic evaluations of track degradation, offering a comprehensive approach to diagnosing track structures and their components.