## 3 Infrared and Visible Image Fusion
### Fusion Rules for High-Frequency Subbands
There is a possibility of improving U-Net or employing other semantic segmentation models to achieve better classification results, but exploring such possibilities is beyond the scope of this paper. The utilization of infrared images for compensation circumvents the potential issues of shadow interference that arise from direct LED lighting. In practical engineering, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{_Pre_} & \multicolumn{2}{c}{_Re_} & \multicolumn{2}{c}{_Spe_} \\ \cline{2-7}  & **ResU-Net** & **R2U-Net** & **ResU-Net** & **R2U-Net** & **ResU-Net** & **R2U-Net** \\ \hline Infrared image & 0.1991 & 0.2482 & 0.8300 & 0.9910 & 0.9778 & 0.9811 \\ Visible image & 0.2224 & 0.2877 & 0.9615 & 0.9676 & 0.9772 & 0.9841 \\ Algorithm A & 0.2817 & 0.2924 & 0.9965 & 0.9988 & 0.9831 & 0.9839 \\ Algorithm B & 0.1963 & 0.2174 & 0.9867 & 0.9818 & 0.9732 & 0.9770 \\ Proposed method & 0.5178 & 0.5884 & 0.9682 & 0.9647 & 0.9940 & 0.9955 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The semantic segmentation network evaluation results. Figure 9: Visualization of R2U-Net recognition results; the gray areas are missed detected pixels, and the red areas are falsely detected pixels. mechanical devices or vehicle-mounted equipment can be designed to accommodate both visible and infrared cameras for inspections. This setup enables the direct acquisition of high-quality detection samples under low-light conditions. The method proposed in this paper exhibits practical application potential in the field of tunnel inspection. ## 5 Conclusions

To address the challenges posed by low-lighting conditions, including low contrast and poor crack recognition accuracy in tunnels, this paper presents a preprocessing method that utilizes both infrared and visible images as detection samples. The aim is to enhance the performance of semantic segmentation networks in such scenarios. Initially, the DAISY descriptor is employed to align the feature points in both images. Subsequently, a multiscale fusion method based on DT-CWT (dual-tree-complex wavelet transform) is developed to effectively combine the features extracted from the infrared and visible images. The pre-processed images are then fed into a semantic segmentation network for crack recognition, and a classical fusion method is introduced for evaluation purposes. The proposed method is validated in a highway tunnel under low-lighting conditions, leading to the following specific conclusions:

1. A multiscale fusion method based on DT-CWT is developed, which incorporates separate fusion rules for low-frequency and high-frequency subbands. The fusion rules for low-frequency subbands utilize pixel saliency, while the fusion rules for high-frequency subbands utilize gradient difference. As a result, the fused image retains the high crack resolution observed in visible images while incorporating the background blurring effect from infrared images. This approach effectively enhances the contrast of the crack location. 2. In scenarios where there is a difference in brightness range and resolution between the infrared and visible images, both the DAISY descriptor and SIFT demonstrate accurate feature point-matching capabilities between the two images. When utilized in conjunction with perspective transformation, these techniques enable successful alignment of the images. 3. Two semantic segmentation models, ResU-Net and R2U-Net, were used to evaluate the effect of image fusion. By analyzing the results of crack recognition and assessing objective metrics like precision, recall, and specificity, it can be concluded that the image preprocessed using the proposed method shows a decreased false detection rate and missed detection rate compared to methods that utilize the original image and other classical fusion algorithms. This makes it more suitable as a detection sample for semantic segmentation networks. Conceptualization, F.W. ; Methodology, F.W. ; Software, F.W. ; Formal analysis, F.W. ; Investigation, F.W. ; Resources, F.W.