# ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation
## 2 Related Work
### Segmentation Model Evolution  
Regarding the evolution of classical segmentation models, Fully Convolutional Networks (FCNs) [29] were the first to introduce end-to-end, pixel-level prediction frameworks, thereby laying the groundwork for modern semantic segmentation. U-Net [30] advanced this concept by proposing an encoder-decoder architecture with skip connections, achieving notable success in biomedical image segmentation and inspiring subsequent model designs. Building on these foundations, multi-scale feature fusion strategies have been widely adopted to further enhance segmentation performance. The DeepLab series [11] employed atrous (dilated) convolutions to expand the receptive field without compromising spatial resolution. PSPNet [31] introduced a Pyramid Pooling Module to effectively capture multi-scale contextual information. More recently, HRNet improved segmentation accuracy by maintaining high-resolution representations through parallel multi-branch architectures, enabling the precise localization of fine structural details.  
[PERSON] et al. [32] applied HRNet to pavement crack detection and demonstrated the advantages of multi-resolution feature fusion for identifying elongated cracks under complex background conditions. However, conventional CNN-based models often rely on local convolutional operations, which struggle to simultaneously achieve global semantic  
Figure 1: Workflow of the proposed pavement crack detection framework.  
understanding and fine-grained representation, particularly when cracks are morphologically diverse, sparsely distributed, or embedded in noisy surfaces [10]. For example, although U-Net preserves low-level features through skip connections, its fixed receptive field restricts its ability to capture the global topological continuity of cracks across varying scales. DeepLabv3+ extends contextual awareness through atrous convolutions, yet remains vulnerable to false positives caused by background surface noise and exhibits inadequate continuity modeling for slender, elongated cracks. PSPNet incorporates pyramid pooling for multi-scale context aggregation, but its coarse-grained feature integration tends to overlook small or subtle crack patterns. Even though HRNet excels at maintaining high-resolution features via parallel multi-branch structures, its reliance on traditional convolutions limits its capacity to model long-range dependencies in complex scenes.  
Recent work by [PERSON] et al. [33] introduced DCRNet, a dual-context residual network that jointly models local detail and global structure using parallel pathways. This dual-path design aligns closely with our use of the EHAT and CSHAM modules, which aim to enhance crack connectivity and multiscale representation. DCRNet has shown strong performance in capturing complex crack morphologies and thus provides a meaningful comparative reference for dual-context segmentation architectures.  
These observations underscore two persistent challenges in CNN-based crack detection: (1) insufficient global perception to capture long-range crack structures, and (2) limited local feature representation for accurately identifying fine, fragmented, or intersecting cracks.