# ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation
## 2 Related Work
### Attention Mechanisms  
The introduction of attention mechanisms has provided new opportunities for addressing the challenges of feature selection and fusion in pavement crack detection. Early methods such as SENet [13] utilized global average pooling to capture inter-channel dependencies and dynamically reweight channel responses. However, due to the absence of spatial interaction, SENet remains insufficient for detecting elongated or spatially distributed crack structures. CBAM [14], which incorporates both channel and spatial attention, improves segmentation performance by focusing on locally salient features. Yet, it still lacks the capability to model long-range spatial dependencies, which are critical for capturing the continuity of dispersed crack segments.  
To enhance global context modeling, DANet [20] introduced a dual-path attention structure, while non-local modules [34] employed self-attention mechanisms to establish pixel-level global correlations. Despite these advancements, many existing approaches rely on fixed-weight fusion strategies. For instance, in the work by [PERSON] et al. [35], CBAM and the non-local module are combined in series, yet the static integration scheme fails to adapt to the morphological diversity of cracks. In contrast, [PERSON] et al. [36] proposed a dynamic convolutional attention network that employs learnable weights to adaptively assign attention across features, offering a promising approach for handling multi-scale and complex crack patterns.  
[PERSON] et al. [37] proposed CrackCLF, a closed-loop feedback-based segmentation network that iteratively refines predictions by incorporating previous outputs as inputs. This dynamic correction mechanism complements our adaptive attention design and represents a promising direction for improving segmentation stability in noisy environments.  
The design of attention mechanisms is especially critical in pavement crack detection, where the structures of interest are typically slender, elongated, and oriented in diverse directions. Detection algorithms must therefore balance the preservation of local detail with the need for global continuity [38]. While traditional attention modules such as CBAM are effective in enhancing local contrast, they often fail to establish relationships between spatially separated crack fragments. Recent studies suggest that directional and topological cues are key to improving detection accuracy. In particular, axial attention has been shown to enhance the recognition of horizontal and vertical crack components by independently modeling one-dimensional spatial dependencies [39].  
Furthermore, [PERSON] et al. [40] proposed an edge-aware attention network that explicitly enhances boundary preservation and continuity through guided refinement. This is particularly relevant to the directional and topological modeling objectives of our EHAT and CSHAM modules.  
Nonetheless, many attention mechanisms continue to employ fixed-weight configurations, which limits their adaptability across diverse scenes. In environments where reticular and linear cracks coexist, this rigidity results in suboptimal segmentation performance [22]. Therefore, the development of dynamically adaptive attention mechanisms capable of modeling multi-scale, morphologically diverse crack structures remains an open and significant research challenge.