## 4 Al and ML in Bridge Inspection
### Other Techniques for Bridge Inspection
The second method was better for large damage (corrosion). In contrast, the first method was successful for large damage (like corrosion). [PERSON] et al. [170] developed a bridge crack detection model that outperformed the traditional Otsu and Niblack methods in achieving better accuracy, precision, and F1 score values. This model was developed on Median Absolute Deviation in images, and the pre-processing steps were highly restricted. Crack edges were determined using Laplace as the detection method, while width was obtained for each centreline point. The structure's performance was calculated using the probability of detection curves that revealed actual crack size, and the model was efficient in detecting cracks of 0.1 mm narrowness with 94% probability. In contrast, cracks of larger width were 100% detected. [PERSON] et al. [171] proposed the combination of drones and DL methods for automatic crack detection of bridge decks. A three-tier framework that embodied orthomosaic map generation, crack detection oriented on DL, and georeferencing and visualisation in a geographic information system (GIS) platform was proposed. Georeferencing coordinates were vital in segmenting, identifying, and extracting lateral impregnated cracks into a GIS platform. This step enabled magnified visualisation and spatial crack analysis. The model was applied to a real bridge, and the outcomes revealed that it can detect and analyse cracks accurately and efficiently. Also, this framework is adaptable and can be applied to diverse infrastructure types. To counterbalance the obstacles of traditional image processing that conduct crack detection based on pixels, [68] used more practical units of measurement (millimetres) to measure concrete cracks in bridges. The investigation followed three steps that harnessed DL and image processing methods for conducting crack classification, segmentation, and its measurement. Custom CNN and U-Net were used for crack classification and segmentation in the first two stages. The final module used a laser calibration method that facilitated the measurement of a crack in millimetres. The main absolute error for crack was described to be 0.16 mm. This showed that combining Deep Learning with laser calibration is a promising solution. Also, because the measurement is carried out in millimetre units, it provides details of structural damage better than the conventional pixel-based methods, making this type of experimentation more practical in the field. [PERSON] et al. [172] conducted a crack detection study on bridge decks, concrete cubes, and walls. The proposed algorithm was randomly tested on 825 images extracted from two random datasets, viz., the authors themselves obtained 225 concrete cube samples, and 600 images were obtained from the open dataset SDNET2018 [120]. The dataset had images with crack sizes ranging between 0.06 mm (narrow) and 25 mm (wide). A 16 MP Nikon camera was used to take these images from a distance of 500 mm without zoom. The 225 images taken by the authors of concrete cubes were captured with an iPhone 7 Plus, consisting of dual 12 MP wide-angle and telephoto cameras. [PERSON] et al. [173] investigated the efficacy of combining DL and UAV photography in the pixel-level crack detection of concrete bridges. The model used a ResNet-18 and the pyramid scene praising network (PSPNet). The study was aware that cracks on the surface of the bridge can have a variety of shapes and backgrounds; therefore, a spatial position self-attention module was introduced in PSPNet to improve its detection accuracy. The model performed well in most common UAV-detected problems in scenarios of improper light, background roughness, different crack types, and heterogeneous background conditions. [PERSON] et al. [174] carried out an automatic pixel-level crack detection study using deep CNN and, in parallel, also executed quantification of crack morphology at the pixel-level.