###### Abstract  
Computer vision methods have been widely used in recent years for the detection of structural cracks. To address the issues of poor image quality and the inadequate performance of semantic segmentation networks under low-light conditions in tunnels, in this paper, infrared images are used, and a preprocessing method based on image fusion technology is developed. First, the DAISY descriptor and the perspective transform are applied for image alignment. Then, the source image is decomposed into high- and low-frequency components of different scales and directions using DT-CWT, and high- and low  
based on deep learning have gradually been applied to the identification of tunnel lining cracks [6,7]. Semantic segmentation networks such as U-Net are capable of achieving pixel-level crack recognition, which can lead to more refined crack recognition results compared to those using the frame output mode. [PERSON] et al. [8] replaced the encoder in U-Net with a pre-trained ResNet-34 block, achieving good recognition performance in tests using the CFD and Crack500 datasets. [PERSON] et al. [9] optimized U-Net by introducing a clique block and an attention mechanism, improving the accuracy of detecting cracks in tunnels. However, due to the limited tunnel lighting conditions and the lining surface complexity, the quality of input visible images used for detection is usually poor. The detection accuracy potential of a semantic segmentation network is limited, and efforts to optimize the semantic segmentation network may not lead to the desired detection results. In computer vision detection techniques, there are many ways to improve the detection effect for different problems. The most discussed area is to improve the network structure of recognition models, and the research hotspot focuses on model optimization for deep learning. Meanwhile, in the whole detection process, sample preprocessing is a necessary task. Common image preprocessing operations include filtering, histogram transformation, and morphological operations, which are all based on the operation of the visible image itself and are easily limited by the quality of the source image. The image fusion technique is a common preprocessing method that is widely used in medical imaging [10], remote sensing imaging [11], and target detection [12,13]. Visible images have better resolution and texture details. The differences and complementarities between infrared and visible images make them common data sources in image fusion work. In recent years, fusion strategies based on multi-scale decomposition have been developed rapidly, and many scholars have developed fusion methods for different scenarios. [PERSON] et al. [14] proposed a multi-scale decomposition image fusion method based on a local edge-preserving (LEP) filter and saliency detection; the fusion process eliminates artifacts and halos in visible images under low illumination by preserving the brightness of the infrared image. [PERSON] et al. [15] use the discrete wavelet transform to implement multi-scale decomposition and enhance the spatial resolution of infrared spectra by extracting visual details from visible images. In addition, there are decomposition methods based on image pyramids [16] and NSCT [17] that have been applied to fusion algorithms. It can be seen that the multi-scale transform can separate the feature coefficients of the infrared image and visible image and then design the fusion rule to transform and combine the discrete coefficients in each scale level. Therefore, the design of fusion rules is the key to the multi-scale fusion algorithm. The formulation of fusion rules depends on the image features and application scenarios. [PERSON] et al. [18] fuse high-frequency subbands using large neighboring pixel differences while combining weighted average and absolute value extraction to fuse low-frequency subbands. [PERSON] et al. [19] proposed a minimum regional cross-gradient method for bandpass direction subband coefficient selection. [PERSON] et al.