## 3 Methodology
### FasterNet Backbone  
In the design of rail defect detection models, both the efficiency of feature extraction and the capacity for multi-scale representation in the backbone network profoundly influence the system's real-time performance and robustness. This work adopts FasterNet--optimized via a coordinated use of Partial Convolution (PConv) and Pointwise Convolution (PWConv)--as the backbone. The core principle hinges on systematically optimizing operators and hierarchically aggregating features to balance efficient computation and high-discriminability feature expression under constrained computational resources. PConv selectively applies standard convolutions to a subset of input channels, thereby significantly reducing computational redundancy and memory access. More concretely, only a small set of consecutive channels is processed spatially, while other channels remain identity-mapped. This strategy preserves global feature integrity while cutting FLOPs for conventional convolution and mitigating I/O bandwidth limitations. Figure 2 illustrates the PConv module structure. Figure 1: Overall architecture of the PerMSCA-YOLO model. The design of this architecture follows the standard paradigm of the YOLO series, which includes a backbone feature extraction network, a feature fusion network, and an output detection head. In the proposed approach, the input image undergoes preliminary down-sampling and channel expansion in the Embedding Layer, followed by alternating stacks of multi-stage FasterNet Blocks and the Merging Layer to efficiently extract both local and global features. Next, MSCAM is introduced at each scale to further focus on track defects and suppress noise. This is then combined with operations such as C2f, Conv, Upsample, and Concat to fuse shallow textures with deep semantics. Finally, perceptual loss is incorporated to constrain the consistency between predicted and ground-truth bounding boxes in the deep feature space, yielding multi-scale defect detection results. To further integrate channel information, the local features produced by PConv undergo PWConv for global channel interaction. By exploiting the linear combination property of a 1 \(\times\) 1 convolution kernel, PWConv dynamically fuses spatial features extracted by PConv with channels skipped during convolution, generating composite representations that unite spatially focused and channel-complete attributes. This mechanism offsets any feature loss introduced by partial convolution, while parameter sharing markedly decreases the complexity of traditional \"T\"-shaped convolutions, enhancing both computational efficiency and representational strength. Figure 3 depicts the comparison between the PConv + PWConv approach and the conventional T-shaped convolution. FasterNet's overall structure comprises multiple FasterNet Blocks, each containing one PConv layer and two PWConv layers to form a \"T\"-type convolution arrangement, as shown in Figure 4.  
The architecture leverages a hierarchical design, employing an Embedding Layer (a 4 \(\times\) 4 convolution with stride 4) and a Merging Layer (a 2 \(\times\) 2 convolution with stride 2) to sequentially downsample spatial dimensions while expanding channels, producing a four-stage feature pyramid. Within each stage, multiple FasterNet Blocks are stacked, with each block forming an inverted residual structure with one PConv layer and dual PWConv layers. The middle layer broadens channels to augment nonlinearity, while identity shortcuts ensure stable gradient propagation. Batch normalization (BN) and ReLU activation function only act on intermediate PWConv layers, thus preventing excessive normalization from undermining feature diversity and allowing for fused BN-convolution operations that accelerate inference. Notably, the final two stages receive additional compute resources based on empirical findings indicating that deeper layers, having reduced spatial dimen  
Figure 3: Comparison between PConv + PWConv and T-shaped convolution.