# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 1 Introduction  
Cracks are one of the primary manifestations of pavement distress, and timely detection and repair of cracks are critical for preventing further deterioration of road conditions and ensuring traffic safety [1, 2]. In practical engineering, road inspections involve a large workload, as they cover extensive road networks, and cracks often exhibit variable shapes with irregular edges. This makes efficient and high-precision crack segmentation a significant challenge. Early methods for road crack detection primarily relied on manual inspections, which were inefficient and highly dependent on the experience of inspectors. This approach could not meet the growing demand for automation in modern road inspections. In recent years, with the rapid development of computer vision technology, high-precision automated crack detection has become feasible [3]. Crack segmentation methods based on computer vision can be broadly categorized into two types: traditional digital image processing methods and deep-learning-based approaches. Digital image processing methods rely on prior information and manually designed feature-extraction techniques [4, 5]. However, their effectiveness is often limited by the experience and domain knowledge of developers, leading to poor generalization and low resistance to interference, making them unsuitable for large-scale road inspection tasks. With the rapid advancement of Convolutional Neural Networks (CNNs) and computational power, deep-learning-based semantic segmentation methods have gradually become the mainstream approach in crack segmentation. These methods can automatically learn features, reducing reliance on prior information and achieving outstanding segmentation accuracy. [PERSON] et al. [6] proposed the Feature Pyramid and Hierarchical Boosting Network (FPHBN) for pavement crack detection, which integrates multi-scale features through a feature pyramid and mitigates class imbalance using a reweighting strategy. [PERSON] et al. [7] developed a crack segmentation method based on an improved UNet architecture, achieving superior performance compared to other UNet variants. [PERSON] et al. [8] designed a lightweight network, DepthCrackNet, which incorporates a dual convolutional encoder structure and a spatial depth enhancement module, significantly improving the extraction of crack detail information. [PERSON] et al. [9] introduced a lightweight network, GMD-Net, which integrates multi-scale convolutional attention mechanisms and outperforms traditional large-parameter models in segmentation accuracy, highlighting the potential of lightweight networks in crack segmentation tasks. However, current methods still lack effective optimization for challenging regions such as crack edges and require further improvement in balancing segmentation accuracy with inference speed. To address the specific challenges of crack segmentation, we optimized the model in three key areas: feature extraction networks, sampling processes, and loss functions. First, to maintain a lightweight architecture, we improved EfficientNet [10] by pruning and adding skip connections, enhancing the network capacity to utilize spatial information. Second, to prevent the loss of image details that occurs with traditional sampling methods, we replaced the original sampling mechanisms with Haar Wavelet Down-sampling (HWD) [11] and Carafe up-sampling [12], which better preserve image details. Finally, we employed a dynamic weighting strategy for the loss function, allowing the model to gradually focus on difficult-to-segment areas, such as crack edges. The main contributions of this paper are summarized as follows:  
1. A lightweight spatial information-enhanced fully convolutional neural network is proposed, achieving advanced segmentation accuracy and model capacity. 2. The model improves up-sampling and down-sampling processes by combining adaptive feature reassembly and wavelet transform, enhancing capacity to capture spatial information from images. 3.