## 4 Conclusions  
In this study, we have presented a novel method for pavement distress classification, designed specifically to handle the unique challenges posed by the sparse distribution of features in road images. The traditional image classification models used in computer vision often fail to capture these sparse features effectively, leading to suboptimal performance when applied to tasks such as road distress detection and classification. Our model, equipped with a specially designed sparse feature module and a sparse constrained loss function, shows superior performance in tackling this problem.  
The experimental results indicate that our model outperforms other state-of-the-art models in terms of classification accuracy for various road distress types, especially for sparsely distributed features such as cracks and potholes. The improved performance can  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Crack** & **Pothole** & **SealedCrack** & **Avg** \\ \hline ResNet & 0.92/0.99 & 0.92/0.79 & 0.88/0.95 & 0.90/0.87 \\ VGGNet & 0.93/1.00 & 0.92/0.86 & 0.87/0.87 & 0.90/0.91 \\ DenseNet & 0.93/1.00 & 0.92/0.79 & 0.92/0.92 & 0.92/0.90 \\ EfficientNet & 0.96/0.99 & 1.00/0.79 & 0.92/0.92 & 0.96/0.90 \\ HRNet & 0.97/0.99 & 0.92/0.79 & 0.89/0.89 & 0.92/0.89 \\ RexNet & 0.98/1.00 & 1.00/0.71 & 0.93/0.97 & 0.97/0.89 \\ SparseModule & 0.96/1.00 & 1.00/0.79 & 0.90/0.95 & 0.95/0.91 \\ Ours & 0.98/1.00 & 1.00/0.86 & 0.88/0.95 & 0.95/0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Precision/recall comparison with state-of-the-art methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Crack** & **Pothole** & **SealedCrack** & **Avg** \\ \hline ResNet & 0.96 & 0.85 & 0.91 & 0.90 \\ VGGNet & 0.96 & 0.89 & 0.87 & 0.90 \\ DenseNet & 0.97 & 0.85 & 0.92 & 0.91 \\ EfficientNet & 0.98 & 0.88 & 0.92 & 0.92 \\ HRNet & 0.98 & 0.85 & 0.89 & 0.90 \\ REXNet & 0.99 & 0.83 & 0.95 & 0.92 \\ SparseModule & 0.98 & 0.88 & 0.92 & 0.92 \\ Ours & 0.99 & 0.92 & 0.91 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(F1\) score comparison with state-of-the-art methods.  
be attributed to the effective extraction and focus on these sparse yet crucial features in the image.  
Additionally, the sparse constrained loss function provides a more refined control over the learning process, guiding the model to focus on the sparse target areas and ignore the largely monotonous background. This results in a more accurate and effective distress classification, as demonstrated by the high average \(F1\) score achieved by our model.  
This research provides paving the way for further optimization of image classification models for similar tasks. Future work can extend this methodology to other sparse feature problems within the domain of road detection.  
The success of our model underlines the significance of incorporating domain-specific knowledge in the design of image classification models, encouraging continued exploration of specialized models to tackle domain-specific challenges effectively. This work also holds practical implications for transportation authorities, offering a more reliable, efficient, and scalable method for road maintenance and safety assessment.