# ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation
###### Abstract  
Accurate segmentation of pavement cracks from high-resolution remote sensing imagery plays a crucial role in automated road condition assessment and infrastructure maintenance. However, crack structures often exhibit asymmetry, irregular morphology, and multi-scale variations, posing significant challenges to conventional CNN-based methods in real-world environments. Specifically, the proposed ETAFHrNet focuses on two predominant pavement-distress morphologies--linear cracks (transverse and longitudinal) and alligator cracks--and has been empirically validated on their intersections and branching patterns over both asphalt and concrete road surfaces. In this work, we present ETAFHrNet, a novel attention-guided segmentation network designed to address the limitations of traditional architectures in detecting fine-grained and asymmetric patterns. ETAFHrNet integrates Transformer-based global attention and multi-scale hybrid feature fusion, enhancing both contextual perception and detail sensitivity. The network introduces two key modules: the Efficient Hybrid Attention Transformer (EHAT), which captures long-range dependencies, and the Cross-Scale Hybrid Attention Module (CSHAM), which adaptively fuses features across spatial resolutions. To support model training and benchmarking, we also propose QD-Crack, a high-resolution, pixel-level annotated dataset collected from real-world road inspection scenarios. Experimental results show that ETAFHrNet significantly outperforms existing methods--including U-Net, DeepLabv3+, and HRNet--in both segmentation accuracy and generalization ability. These findings demonstrate the effectiveness of interpretable, multi-scale attention architectures in complex object detection and image classification tasks, making our approach relevant for broader applications, such as autonomous driving, remote sensing, and smart infrastructure systems. p 2025 25 April 2025 23 May 2025 experienced inspectors can recognize visible damage, manual approaches are difficult to scale and insufficiently accurate for large road networks. In contrast, automated vision-based systems have reduced false positive rates to under 5% [3], offering a promising direction for smart pavement monitoring. Traditional methods based on threshold segmentation [4] or edge detection [5] perform poorly under complex lighting and noise conditions. The emergence of deep learning has led to substantial progress in crack segmentation. Encoder-decoder networks such as U-Net [6] enable end-to-end detection. DeepCrack [7], for instance, achieves high IoU through multi-scale fusion but struggles to retain fine-grained structural details. To improve spatial resolution, HRNet [8] was introduced, maintaining high-resolution representations via parallel branches. [PERSON] et al. [9] and [PERSON] et al. [10] adopted multi-resolution and adaptive thresholding strategies, yet their models still underperform in detecting fine or net-like cracks. In recent years, researchers have increasingly adopted attention mechanisms and Transformer-based architectures to improve global perception and feature representation in crack segmentation tasks. [PERSON] et al. [11] and [PERSON] et al. [12] introduced channel and non-local spatial attention, significantly enhancing discriminability and context awareness. However, these methods typically incur high computational overhead. Further developments such as SENet [13], CBAM [14], and Pyramid Attention Networks [15] improved adaptability to crack morphology but still lack flexible weighting mechanisms and robust generalization under noisy backgrounds. In the Transformer domain, ViT [16] enables long-range modeling but demands extensive computation. Swin Transformer [17] reduces complexity via window partitioning but compromises spatial continuity. SegFormer [18] merges CNN and Transformer strengths, achieving balanced performance, but fixed attention fusion often weakens fine-detail segmentation [19; 20]. Recent improvements by [PERSON] et al. [21], [PERSON] et al. [22], and [PERSON] et al.