###### Abstract
[20] use the non-sampling shear transform (NSST) to select the fusion coefficients of the background in order to capture the details of the visible image. In the multi-scale decomposition method based on dual-tree complex wavelet transform (DT-CWT), [PERSON] et al. [21] use swarm intelligence based on particle swarm optimization to find the best weights. [PERSON] et al. [22] utilized fuzzy logic to integrate the outputs of three different fusion rules and proposed a new method based on population optimization for the design of low-frequency fusion rules. The above scholars have designed different fusion rules based on the decomposition method, but the core idea is roughly the same, which is to extract the brightness of the infrared image and the details of the visible image for fusion processing. Infrared images reflect the thermal radiation properties of an object and are not affected by the environment, such as lighting, and their ability to highlight a target is better. In the field of crack detection studied in this paper, preprocessing methods based on image fusion techniques have been found to be effective in improving the performance of recognition models [23]. [PERSON] et al. [24] used an infrared image fusion technique for pavement crack detection based on deep learning to solve the problem of uneven illumination as well as shadows on the pavement. [PERSON] et al. [25] and [PERSON] et al. [26] enhanced image information by fusing thermal infrared (IRT) images and visible images and applied this information to crack recognition on concrete surfaces. The above scholars' work validates the feasibility of using image fusion techniques for feature enhancement. The shooting conditions inside tunnels are more complex compared to those of pavement and concrete surfaces. First, the light inside tunnels is darker, resulting in a low contrast at the crack locations in visible images [27]. In addition, the flatness of the lining surface and the ambient light result in more prominent details in the image background. Directly using LED or similar components for supplementary lighting can result in uneven brightness distribution in the image and introduce shadow interference. When using semantic segmentation networks for crack recognition, all of these features lead to the degradation of the imaging quality and affect the accuracy of the model recognition. Therefore, the development of an image preprocessing method based on image fusion techniques for tunnel environments is of great significance for computer vision detection. In this paper, an image preprocessing method is developed using image fusion techniques that introduce infrared images to enhance the crack target information. First, the DAISY descriptor is used to match the image feature points and then combined with the perspective transform to achieve image alignment. Then, the multiscale fusion method is developed based on dual-tree complex wavelet transform (DT-CWT), and the fusion rules of low-frequency subbands and high-frequency subbands are designed according to the image characteristics in the tunnel environment. Finally, the fusion results are input to the semantic segmentation network for performance evaluation. The remainder of this paper is organized as follows. Section 2 describes the feature extraction and alignment process of the image. In Section 3, the high- and low-frequency fusion rules designed based on DT-CWT are presented, and in Section 4, the fusion results are evaluated and analyzed using a semantic segmentation network.