## 2 Related Work
### Deep Learning Methods  
With the rapid advancements in deep learning for image recognition and object detection, an increasing number of researchers have applied convolutional neural networks (CNNs) and their derivatives to the task of track defect detection. These methods typically eliminate the need for manual feature design, instead relying on end-to-end training with vast amounts of data to automatically learn high-level features that exhibit strong discriminative power for defect identification. In practice, researchers often incorporate lightweight or multi-scale strategies to improve mainstream detection frameworks, such as YOLO, SSD, and Faster R-CNN, balancing the diversity of track defects with the need for real-time detection. [PERSON] et al. [25] achieved notable improvements in steel defect classification using a max-pooling convolutional neural network, laying both theoretical and practical foundations for subsequent CNN applications in track defect detection. [PERSON] et al. [26] proposed a two-stage detection framework based on deep convolutional networks for detecting defects on track surfaces and fasteners: initially, a fully convolutional network is employed to quickly identify candidate defect regions, followed by refinement using a residual network for precise classification and localization, achieving high accuracy in surface and fastener defect detection. [PERSON] et al. [27], addressing the issue of excessive parameter count in the YOLOv4 model, integrated MobileNetv3 into the backbone network and employed depthwise separable convolutions, successfully reducing model size and improving inference speed. Additionally, their improved model demonstrated significant enhancement in railway surface defect detection accuracy. In a similar vein, [PERSON] et al. [28]incorporated the Support Vector Data Description (SVDD) algorithm into Faster R-CNN to perform secondary classification of misaligned fasteners, effectively reducing detection errors caused by angular shifts, showcasing the powerful scalability of deep learning for fastener detection. Within the YOLO series models, some researchers have focused on overcoming challenges such as detecting small target defects and suppressing complex backgrounds. [PERSON] et al. [29] introduced the SPD-Conv module and the Focal-SloU loss function into YOLOv8 to enhance the network's ability to capture small defects on track surfaces, integrating a lightweight attention mechanism to ensure practicality. [PERSON] et al. [30], addressing concrete crack detection, analyzed the compatibility of YOLOv2 with various CNN feature extractors, demonstrating that selecting the appropriate shallow or mid-layer network can significantly improve detection efficiency in data-limited scenarios. For industrial metal base defect detection, [PERSON] et al. [31] proposed the YOLO-SO algorithm, which incorporated the Convolutional Block Attention Module (CBAM) and random paste-mosaic data augmentation, increasing sensitivity to small defects. For the detection of critical components such as high-speed rail fasteners, [PERSON] et al. [32] designed a defect detection model based on the improved YoLoX-Nano, utilizing coordinate attention (CA) mechanisms and adaptive spatial feature fusion (ASFF) to achieve precise localization and real-time detection of track fasteners. Additionally, [PERSON] et al. [33] proposed a multi-task learning strategy from the perspectives of material classification and semantic segmentation, allowing simultaneous detection and segmentation of track regions, ballast, and fasteners, offering valuable insights for subsequent defect identification. [PERSON] et al. [34] made improvements to the original YOLOv3 by introducing pruning and dense connections, constructing a lightweight model for multi-target track defect detection, which meets the demands of high-speed inspection.