## 4 Al and ML in Bridge Inspection
### Other Techniques for Bridge Inspection
\\  & & & & & Environmental variables, including light fluctuations, shadows, and surface reflections, can affect the accuracy of crack detection during \\  & &[PERSON] et al. [164] proposed a lightweight, real-time, pixel-level crack detection method based on an improved instance segmentation model. The new prototype mask branch ProtoC1 keeps the instance mask quality identical to the original branch, significantly reduces the parameters and complexity in the prototype mask branch, and speeds up the processing speed of the prototype branch. The reduction in volume, with minimal computational prerequisites makes it practically suitable for low-lying devices. Training cost was minimised, and enhanced robustness was achieved by applying a cross-domain transfer learning strategy. [PERSON] et al. [24] conducted a unique study deploying AI tools with the most trending techniques of drone technology and 3D printers to carry out SHM for bridges. They emphasised replacing visual inspection with added technology because they could study the condition of bridges from all possible angles and figure out specific damage caused by their high flight altitude. They can fly in different weather conditions, are equipped with high-tech cameras, and can take pictures continuously while monitoring bridges, which can later be processed by ML, DL, and the Internet of Things. [PERSON] et al. [165] detected bridge cracks using an ML system based on deep super-resolution segmentation. Accurate crack identification was achieved at 100\(\upmu\)m by combining images of two forms. Sub-surface bridge damage and delamination were detected with less input effort and costs. Poor bridge condition was held responsible for such a false indication. Along with a unique ML method, infrared images were also combined to obtain good-quality results. [PERSON] et al. [166] garnered a fruitful and effective combination of a Short-Term Dense Concatenate Network (STDC-Net) and a refinement network developed to explore and study cracks using bridge surface crack segmentation. It improved the crack detection rate and fully harnessed information obtained from binary cross-entropy loss and dice loss. The entire setup was used to observe and unravel segmentation results by refining the segmentation results provided by the offset map based on the training and test stages. The STDC-Net and a refinement network were combined to create a new dense boundary refinement network (DBR-Net). In the case of real-time, the crack detection rate was as good as 37.0 images/s. The work of [PERSON] et al. [167] considered that damage in dark or internal places often goes unnoticed by conventional images. This study tried to overcome this using thermal (infrared) images and ML models. These images were fed to a VGG 16 model and exhibited enhanced righteousness as chances of false detections were minimal. The results from two concrete bridge decks proved that many varied damage types could be detected by MobileNetV2 in thermal images, identifying 79.7% of damage, including patches, delamination, cracks, and spalling on concrete. [PERSON] et al. [168] conducted extensive and detailed research on the most recent available trends and limitations, where DL algorithms can be utilised for SHM in the case of bridges. The study showed that vibration-based and vision-based processes can be used for damage detection/diagnosis. With the assistance of this extracted reinforcement information, a contour map displaying bridge deck corrosion could be created. [PERSON] et al. [169] directed their study to explore the application of a U-Net to find bridge corrosion and cracks. This study used a pre-trained VGG architecture to pre-train the U-Net, using compressed high-resolution input images. Two image input methods were covered to obtain the segmentation results. One approach is Squashing Segmentation, which inputs compressed high-resolution images straight into the VGG-UNet model, whereas Cropping Segmentation uses cropped images sized 224 \(\times\) 224 as input.