# Concatenated Attention: A Novel Method for Regulating Information Structure Based on Sensors
## 3 Results
### Comparison on Crack Dataset  
The dataset applied in this section consists of binary classification data captured by sensors under varying environmental conditions [35]. One class represents surfaces with cracks, while the other class represents intact surfaces. Two sample images from the dataset  
Figure 6: The ROC curve of the MHSA method on the Crack-Segmentation dataset.  
Figure 7: The ROC curve of the CA method on the Crack-Segmentation dataset.  
are shown in Figure 8, where the left image shows a cracked surface, and the right image shows a flawless surface. Notably, the intact surface image highlights one of the key challenges in this task: even pristine surfaces may exhibit stains, water marks, or other environmental artifacts that resemble cracks, complicating the classification process. It is evident that even intact surfaces may have stains or other attachments, which can lead to misclassification, particularly in low-light underwater environments. Additionally, varying illumination levels and sensor noise in these environments exacerbate the difficulty, further stressing the need for robust feature extraction and classification mechanisms.  
The comparative results of the two methods on the Crack dataset are summarized in Table 7. While both methods achieve very high accuracy, neither reaches 100 percent, which underscores the inherent difficulty of the dataset due to ambiguities and visual similarities between classes. Some challenging images in the dataset feature cracks obscured by shadows or dirt, while others show intact surfaces marred by misleading artifacts. Nonetheless, the proposed method achieves a relatively higher average accuracy compared to the original method, demonstrating the advantage of the CA mechanism in extracting discriminative features even under suboptimal conditions. By enriching intermediate representations through weighted summation and recombination, the proposed method enhances the network's ability to distinguish subtle differences between the two classes. In the bootstrap method, the confidence interval in accuracy between the CA method and the MHSA method is \([-0.000075,0.000400]\). The mean difference in accuracy is 0.000175. Importantly, this improvement is achieved without increasing the number of parameters or FLOPs, ensuring that the computational efficiency remains unaffected. This makes the method particularly suitable for resource-constrained real-time applications such as underwater monitoring systems.  
The ROC curve MHSA method and the CA method correspond to Figure 9 and Figure 10, respectively. By comparison, it can be seen that the AUC-ROC of the two methods is 1, which shows that both methods are perfect models. Meanwhile, this also proves that neither model is overfitting or underfitting.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Accuracy** & **FLOPs (G)** & **Param. (M)** \\ \hline MHSA & 99.87 & 4.37 & 27.59 \\ CA & 99.89 & 4.37 & 27.59 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The average accuracy of the comparative experiments on the Crack dataset.  
Figure 8: Samples from the Crack dataset, photos with cracks (**left**) and photos without cracks (**right**).