# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 3 Methods
### Lightweight Fully Convolutional Neural Network  
High-resolution semantic segmentation networks are capable of preserving spatial information in images and delivering precise segmentation results. However, these networks typically require multiple rounds of feature extraction and fusion for high-resolution feature maps, resulting in significantly increased parameter counts and computational demands. Additionally, such complex models are prone to overfitting when applied to specific small-scale scenarios. Previous studies have demonstrated that lightweight networks often outperform complex ones in crack segmentation tasks [9; 19].  
This study focuses on real-world pavement crack detection scenarios, such as road maintenance vehicle-mounted equipment or mobile terminal applications. These devices are typically constrained by limited computational capacity (e.g., ARM processors) and storage resources, necessitating the use of efficient and lightweight models.  
To address these challenges, we propose a lightweight spatial-information-enhanced, fully convolutional neural network for pavement crack segmentation. The network is based on the classic fully convolutional network (FCN) architecture and uses the lightweight EfficientNet-B0 model as the backbone network. EfficientNet-B0 was chosen primarily for its compound scaling strategy, which balances parameter count, computational cost, and accuracy. Furthermore, compared to larger EfficientNet variants, B0 exhibits significantly reduced model complexity, making it more suitable for mobile devices and embedded systems. The core module of EfficientNet is the lightweight MBConv (Mobile Inverted Bottleneck Convolution). MBConv integrates depthwise separable convolutions with the SE (Squeeze-and-Excitation) channel attention mechanism, further enhancing the efficiency and performance of the model. The structure of MBConv is illustrated in Figure 1.  
The original EfficientNet architecture is relatively deep, which can lead to over-abstracted target features and the loss of crucial spatial information. To address this limitation, this study reduces the number of down-sampling operations in the backbone network to four, preserving more spatial details. Over-abstraction often results in excessively smoothed segmentation boundaries, hindering the accurate detection of fine crack features--a critical aspect of crack segmentation tasks. Given that crack segmentation involves only two classes--background and target--the informational complexity is relatively low. Therefore, this study also prunes the number of blocks and feature map channels at each down-sampling stage to align the model's capacity with the simplicity of the input data. The optimized network architecture is illustrated in Figure 2.  
Figure 1: Structure of MBConv and SE Channel Attention Mechanism.  
Figure 2: Optimized Backbone Network Structure.  
In the FCN network, the decoder restores the encoder's output to a segmented image. FCN16s and FCN8s further enhance this process by incorporating the 16\(\times\) and 8\(\times\) down-sampled feature maps into the decoder's up-sampling process, effectively fusing spatial and semantic information to improve segmentation accuracy. For pavement crack segmentation, enhancing the model's ability to perceive and process spatial details is critical. Therefore, this study introduces a strategy where, at each down-sampling stage (2\(\times\), 4\(\times\), 8\(\times\), and 16\(\times\)), feature maps rich in spatial information are repeatedly integrated into the corresponding up-sampling stages via skip connections. This approach facilitates a more effective fusion of deep semantic features with shallow spatial details, enhancing the model's ability to capture intricate crack edges and preventing the loss of fine crack features during the convolutional process. While this method marginally increases computational complexity, it yields substantial improvements in segmentation performance.