# ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation
## 6 Conclusions  
This study proposed ETAFhrNet, a Transformer-enhanced segmentation network specifically designed to tackle the challenges of detecting complex and irregular crack patterns in high-resolution pavement imagery. By integrating the Efficient Hybrid Attention Transformer (EHAT) and the Cross-Scale Hybrid Attention Module (CSHAM) into the HRNet backbone, our model effectively captures both long-range contextual dependencies and fine-grained structural features that are critical for accurate object segmentation and classification. Comprehensive experiments on the self-constructed QD-Crack dataset confirm that ETAFhrNet surpasses state-of-the-art approaches, including U-Net, DeepLabv3+, and HRNet, in terms of segmentation accuracy, precision, recall, and inference speed. Ablation studies demonstrate that the two proposed attention modules provide complementary benefits, particularly in enhancing the representation of directionality, scale variation, and discontinuity, which are typical characteristics of asymmetric visual objects. The proposed framework contributes to the development of interpretable and efficient AI models for infrastructure monitoring, with extensibility to a wide range of applications such as bridge inspection, tunnel lining analysis, and remote sensing-based structural assessment. Moreover, the model's architecture aligns with the broader goals of object detection and image classification, especially under challenging conditions where traditional models struggle. Although ETAFhrNet shows promising segmentation accuracy and inference speed, several practical constraints remain: (1) Data diversity: the QD-Crack dataset mainly contains dry asphalt surfaces captured in daylight; performance under concrete pavements, wet conditions, night-time illumination, and extreme weather has not yet been validated. (2) Micro-crack sensitivity: hairline cracks narrower than two pixels are occasionally missed, revealing insufficient fine-scale feature capture. (3) Pavement-material dependence: preliminary trials on concrete surfaces reveal false positives where aggregate texture is confused with cracks, indicating the need for material-aware domain adaptation. (4) Edge deployment: the current model still relies on an NVIDIA RTX 3070 Ti GPU; additional pruning and quantization are required for real-time inference on low-power edge devices. (5) Continuous video streams: experiments were conducted on discrete images; real-time tracking of cracks in on-board video sequences demands further pipeline optimization. (6) Domain generalization: transferability to geographically distinct road networks or other infrastructure (e.g., bridges, airport runways) remains to be verified through cross-domain testing. Addressing these issues constitutes our immediate future work. Looking ahead, future research will focus on optimizing ETAFhrNet for lightweight deployment on edge devices, enhancing its generalizability across diverse environmental scenarios, and improving its ability to identify micro-scale defects under varying pavement materials. More broadly, our findings emphasize the significance of modeling asymmetry and multi-scale variation in visual data, a principle that is critical for building robust, generalizable, and explainable object-recognition systems across real-world domains. Conceptualization, C.T. and R.L. ; methodology, R.L. and Z.Z. ; software, J.L. ; validation, P.T., A.Y., and C.T. ; formal analysis, C.T. and P.T. ; investigation, S.P. and Z.Z. ; resources, R.L. and Z.Z. ; data curation, J.D. ; writing original draft, C.T., J.L., and Z.Z. ; writing review and editing, R.L. and P.T. ; visualization, S.P. ; supervision, R.L. ; project administration, R.L. ; funding acquisition, R.L. All authors have read and agreed to the published version of the manuscript. This study was supported by the National Natural Science Foundation of China (Grant No. 42001414) and the \"Elite Program\" Research Support Foundation (Grant No. 0104060541613). **Informed Consent Statement:** Not applicable.