## 2 Related Work
### Computer Vision Methods  
With the continuous improvement in image sensor and processor performance, computer-vision-based track surface defect detection methods have gradually emerged as a research hotspot. Compared to traditional manual inspections or single sensor detection techniques, computer vision approaches leverage high-definition cameras, drones, and structured light to capture comprehensive track surface information, followed by multi-stage or hierarchical algorithms for defect recognition and localization. To address the diversity of track surface defects and complex background noise, [PERSON] et al. [15] proposed a coarse-to-fine model (CTFM) that performs defect filtering and localization at the sub-image, region, and pixel levels, significantly reducing background interference and false detection rates. Similarly, [PERSON] et al. [16] developed a layered extractor framework that first removes irrelevant regions with a coarse extractor, then focuses on defect features with a fine extractor, achieving high detection accuracy and speed across multiple public datasets and real-world scenarios. [PERSON] et al. [17] combined curvature filtering with an improved Gaussian mixture model to adaptively filter noise on track surfaces, achieving enhanced defect segmentation accuracy. A common feature of these multi-stage detection approaches is the combination of broad-range searching and local refinement, which effectively improves detection efficiency and precision. With the rapid application of drone technology in traffic monitoring and engineering inspections, [PERSON] et al. [18] explored the use of drones to capture track surface images, proposing the LWLC-GSME image enhancement and threshold segmentation method. Their core idea was to first correct uneven lighting using local Weber contrast (LWLC) and then apply gray-scale stretching maximum entropy (GSME) to segment defect regions, achieving high detection recall rates. These methods significantly expand the scope of track inspections and are suitable for rapid detection in mountainous areas, over rivers, or other regions that are difficult for human access. However, challenges remain in terms of drone flight time, GPS accuracy, and robustness under complex meteorological conditions. In addition to 2D images, some scholars have focused on the three-dimensional morphology of track surfaces to more accurately quantify crack depth or shape. [PERSON] et al. [19] proposed a 3D point cloud reconstruction method based on line-structured light,allowing high-precision measurements of track surfaces, followed by curve fitting models for dynamic online detection. Compared to traditional 2D images, 3D data help reduce the impact of surface texture differences or lighting variations. However, this method requires high hardware specifications and presents challenges in real-time processing of 3D point clouds, demanding more efficient algorithms. Morphological operations and filters continue to play a significant role in the design of visual detection algorithms. [PERSON] [20] proposed a fast detection system based on morphological operations, extracting and matching track surface defect shapes using a multi-resolution morphological pyramid, balancing both detection speed and accuracy. [PERSON] et al. [21] employed adaptive block segmentation based on boundary edge features, combined with an edge growth strategy, successfully addressing the interference caused by uneven lighting and complex backgrounds. [PERSON] et al. [22] conducted studies on filter feature selection, comparing Gabor filters with discrete wavelets, finding that Gabor filters excel in waveform detection scenarios due to their superior time-frequency localization capabilities. [PERSON] et al. [23] earlier proposed a spectral image differential method, differentiating light sources of different wavelengths to highlight surface cracks and delamination regions, effectively combining online and offline detection. [PERSON] et al.