Bad weather conditions (such as fog, haze) seriously affect the visual quality of images. According to the scene depth information, physical model-based methods are used to improve image visibility for further image restoration. However, the unstable acquisition of the scene depth information seriously affects the defogging performance of physical model-based methods. Additionally, most of image enhancement-based methods focus on the global adjustment of image contrast and saturation, and lack the local details for image restoration. So, this paper proposes a single image defogging method based on image patch decomposition and multi-exposure fusion. First, a single foggy image is processed by gamma correction to obtain a set of underexposed images. Then the saturation of the obtained underexposed and original images is enhanced. Next, each image in the multi-exposure image set (including the set of underexposed images and the original image) is decomposed into the base and detail layers by a guided filter. The base layers are first decomposed into image patches, and then the fusion weight maps of the image patches are constructed. For detail layers, the exposure features are first extracted from the luminance components of images, and then the extracted exposure features are evaluated by constructing gaussian functions. Finally, both base and detail layers are combined to obtain the defogged image. The proposed method is compared with the state-of-the-art methods. The comparative experimental results confirm the effectiveness of the proposed method and its superiority over the state-of-the-art methods.

Aiming at the intelligent fault diagnosis of tunnel inspection robot, the T-S fuzzy fault tree method was proposed to build the intelligent diagnosis system. Taking positioning system of the robot as an example, the T-S fuzzy FTA model of the system was established. And the T-S importance of each component and fault checking sequence were calculated using the T-S fuzzy gate rule. The example analysis results show that, the method improves the accuracy and practicability of fault diagnosis of the robot system.

The maintenance of road tunnels is more important, and there are problems such as water seepage, surface cracking and falling off. If it cannot be detected and handled in time, it will pose a major threat to the driving safety of road vehicles. Therefore, this paper proposes a road tunnel defect detection scheme based on laser point cloud. Firstly, a robot for road detection is developed. Secondly, a road defect detection method based on laser point cloud is developed. Laser SLAM technology is used to reconstruct dense point clouds in road tunnel scenes. Finally, through the automatic detection of the three-dimensional reconstruction scene of the tunnel, the defects such as cracks and spalling of the road tunnel are automatically identified. Compared with the visual detection scheme, this method does not depend on the problem of ambient light and has better robustness and practicability.

Tunnel concrete invert is very important to the stability of the entire tunnel structure. However, invert’s quality is usually insufficient due to various reasons. Traditional approaches to detecting tunnel concrete invert focus on inefficient coring. The results of detection often cannot reflect all realities of the invert and concrete fill layer. These approaches are short of meeting current detection requirements for concrete invert. In order to increase the effectiveness and accuracy of invert test this paper summarizes technical features of GPR method and Rayleigh wave method according to concrete structure NDT and tunnel invert test technologies at home and abroad. The results show: the GPR method is simple and efficient and capable of qualitative test of tunnel invert, though with a trade-off between detection depth and accuracy; the Rayleigh wave method provides high accuracy and the qualitative test ability for tunnel invert thickness and unconsolidated fill, though inefficient due to a lack of pertinent equipment.