# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 2 Related Work
### Research on the Combination of Crack Detection and Semantic Segmentation  
In recent years, semantic segmentation has progressively become the mainstream approach for automated road crack detection, owing to its ability to address the limitations of traditional algorithms. [PERSON] et al. [25] proposed a deep supervised convolutional neural network for crack detection, incorporating a multi-scale convolutional feature fusion module. However, its computational efficiency is insufficient for real-time detection. [PERSON] et al. [26] introduced a strip pooling module and an attention-based skip connection strategy, effectively suppressing irrelevant regions and enhancing the network's ability to segment elongated cracks, yet it lacks dedicated optimization for crack edge information. [PERSON] et al. [27] developed a multi-scale crack feature extraction network (MsCFEN), enabling effective capture of both transverse and longitudinal crack features. However, its high demand for computational and storage resources hinders deployment on mobile devices. [PERSON] et al. [28] proposed a crack recognition method that combines feature pyramids with a memory mechanism, significantly enhancing generalization and robustness across multiple datasets, but its accuracy on public datasets still leaves room for improvement.  
As the demand for real-time detection increases, lightweight crack segmentation networks have gained prominence as a research focus. Studies indicate that lightweight networks, by minimizing the impact of redundant information, demonstrate superior performance in crack segmentation tasks [8; 9]. [PERSON] et al. [29] enhanced the encoder structure of U-Net and adopted a fusion strategy with long and short skip connections, reducing network complexity effectively, although deficiencies persist in segmenting fine crack details. [PERSON] et al. [30] proposed a lightweight segmentation network, CrackScopeNet, incorporating multi-scale branches and strip-shaped contextual attention mechanisms to improve sensitivity to cracks of varying sizes. However, its performance is limited for cracks that blend with the background. [PERSON] et al. [31] integrated multi-feature extraction, edge extraction, pixel expansion, attention modules, and efficient feature fusion modules, achieving significant improvements in segmentation accuracy across multiple datasets. Nonetheless, their model still struggles with fine crack recognition. [PERSON] et al. [32] proposed the lightweight crack segmentation model C-UNet by employing a multi-scale cascade strategy and optimizing the model structure. Despite its advancements, the model exhibits poor performance in identifying fine cracks and is prone to misclassification under stain interference. [PERSON] et al. [33] introduced DSDGNet, a weakly supervised method based on dual separation and domain generalization, which improves generalization by generating high-quality crack data and addressing the challenges of crack data annotation. However, the model's computational efficiency still requires enhancement to meet the demands of real-time detection.  
The existing methods exhibit several critical limitations: First, many models overly rely on large-scale parameters to achieve higher accuracy, which constrains their real-time applicability in practical engineering contexts. Second, lightweight methods lack sufficient optimization for crack-specific characteristics. The use of traditional up-sampling and down-sampling strategies leads to significant loss of crack detail during feature extraction and reconstruction, particularly affecting the segmentation of fine cracks. Lastly, most methods utilize cross-entropy as the loss function. Given the severe class imbalance in crack images and the intricate edge features, a single static loss function is inadequate for effectively optimizing model parameters, particularly in capturing crack boundaries and details.