# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 4 Results
### Model Comparison Experiments  
To validate the effectiveness of the proposed model, we compared it with classic semantic segmentation models such as UNet, HRNet, and DeepLabV3+ [18; 19; 20; 21], as well as lightweight models like EDANet and ESNet [37; 38; 39; 40] on the Crack500 test set. Additionally, we compared it with methods from references [8; 31; 33; 41]. The experimental methods were implemented according to Section 4.2, with the cross-entropy loss function used during training. The results of the various models on the evaluation metrics are presented in Table 1. And segmentation results on representative samples are shown in Figure 6.  
The experimental results demonstrate that the proposed model achieves an MIoU of 79.34% and an F1-score of 76.45%, outperforming all the compared models and significantly surpassing lightweight models such as DABNet. In terms of complexity, the proposed model has only 0.23 M parameters and 0.95G FLOPs, achieving high segmentation accuracy with minimal computational complexity. Additionally, its inference speed reaches 73.49 FPS, performing well among lightweight models and exceeding most complex models. Overall, the proposed model achieves an excellent balance between performance and complexity, making it suitable for practical applications. The visualization results indicate that many models struggle with segmenting fine cracks (see left column 1), with some failing to even detect their presence (e.g., HRNet, DeepLabV3+). In contrast, the proposed model accurately captures the contours and edges of fine cracks. On heavily worn pavements (see left columns 3 and 6), some models have difficulty distinguishing between the background and cracks (e.g., UNet, ContextNet), while the proposed model provides the best overall segmentation of both the crack region and its boundaries. On pavements with stains and stone interference (see left columns 5 and 7), some models are influenced by the noisy information, misclassifying noise as cracks (e.g., EDANet, MobileNetV3). However, the proposed model exhibits strong \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MIoU/\%** & **F1-Score/\%** & **Accuracy/\%** & **Params/M** & **Flop/C** & **FPS** \\ \hline UNet & 72.30 & 68.05 & 95.36 & 19.51 & 157.96 & 16.84 \\ DeepLabV3+ & 74.87 & 71.26 & 96.41 & 40.29 & 61.51 & 41.68 \\ HRNet & 76.58 & 71.47 & 97.27 & 29.53 & 80.30 & 21.49 \\ DDRNet & 75.49 & 70.27 & 97.00 & 32.36 & 31.39 & 70.96 \\ ContextNet & 77.49 & 73.28 & 97.34 & 0.87 & 0.78 & 135.97 \\ DABNet & 78.89 & 75.49 & 97.53 & 0.75 & 4.64 & 108.38 \\ EDANet & 78.22 & 74.71 & 97.37 & 0.68 & 3.91 & 87.09 \\ ESNet & 78.65 & 75.49 & 97.49 & 1.66 & 11.82 & 72.04 \\ MobileNetV3 & 78.70 & 75.38 & 97.41 & 2.98 & 1.16 & 119.16 \\ LMM [31] & 74.81 & 74.89 & — & 0.87 & 6.56 & — \\ Wang et al. [41] & — & 76.03 & — & — & — & — \\ DSDGNet [33] & 77.67 & — & — & 2.82 & 32.13 & — \\ DepthCrackNet [8] & 77.00 & 73.83 & 96.3 & — & — & — \\ Ours & 79.34 & 76.45 & 97.50 & 0.23 & 0.95 & 73.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Evaluation Metrics for Various Network Models on the Crack500 Test Dataset. robustness, maintaining excellent segmentation performance. In the segmentation of mesh-like cracks (see left column 4), the proposed model performs comparably well with networks such as MobileNetV3 and HRNet. Overall, the proposed model demonstrates excellent segmentation capabilities across various scenarios. Several models trained on the Crack500 dataset were directly tested on all samples of the AsphaltCrack300 dataset for cross-dataset evaluation, with the results presented in Table 2. Due to changes in data distribution, the accuracy of all models decreased compared to their performance on the Crack500 dataset. However, the proposed model demonstrated outstanding performance in the cross-dataset evaluation, achieving an MIoU of 69.63% and an F1-score of 63.78%, ranking the best among lightweight models and even surpassing some models with larger parameter sizes.