## 4 Al and ML in Bridge Inspection
### YOLO-Based Bridge Inspection Techniques  
Many researchers have focused their work on the YOLO architecture for bridge inspection techniques, which is described in Table 4. The main advantages of YOLO-based solutions are that they are fast, allow near real-time object detection, and achieve high accuracy in several applications. This subsection reviews studies using this architecture. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Objective** & **Technique** & **Dataset** & **Outcome** & **Features/Limitations** \\ \hline \multirow{8}{*}{[152]} & To accurately and autonomously calculate reinforcing and diameter. & The extracted thickness range became the input & EMI data for 1D CNN training. GPR data for & Accuracy obtained in sand pit experiments & Besides CNN, other methods usually used, like ANN, were overlooked. \\  & To accurately and autonomously calculate reinforcing and bar cover thickness & The extracted thickness & The models were trained and validated on a limited dataset. YOLOv5 training. & The models were trained and validated on a limited dataset. Pre-processing data from GPR and EMI in all fields not possible. \\ \hline \multirow{8}{*}{[153]} & Coarse and fine crack analysis of concrete bridges based on (ODNM); double detection and single segmentation. & Object detection network model & U-Net model’s accuracy was 98.37\%. & U-Net is used for semantic segmentation \\  & Coarse and fine crack analysis of concrete bridges based on (ODNM); double detection and single segmentation. & U-Net model’s accuracy was 98.37\%. & Detecting cracks that occupy a very small area relative to the overall image remains challenging. \\  & & U-Net trained using learning, testing, and validation. & The proposed method was better in & Efficiency was better than other normally used methods. \\ \hline \multirow{8}{*}{[154]} & Detecting & STL-ELM & The proposed method was better in & Efficiency was better than other normally used methods. \\  & fine-grained concrete cracks using pseudo-labelling & STL-ELM & Buildings dataset & 2.62\%, and the F1 value improved by & The proposed approach erroneously identified some samples from the no-crack category as images depicting cracks. YOLOv3 was chosen by [PERSON] et al. [152] to study scanned images and their reflected signals of bridges, especially reinforcement types. An overall idea of the thickness range of the reinforcement bar was thus estimated. [PERSON] et al. [153] studied double detection and single segmentation preliminary detection of crack images using a Deep Learning-based initial crack detection network. A few rectangular boxes that contained cracks were obtained. Then, in each rectangular box, a Deep Learning-based fine crack detection network was employed to identify and locate the actual detected crack region to extract the accurate location of the crack. The detected cracks were then put through image semantic segmentation to segment the identified cracks. There were two detection stages. In the course detection stage, the complex background was excluded, leading to the identification of regions that contain cracks. The results from this stage were further processed during the fine detection stage, reducing false detection and enhancing detection accuracy. In the segmentation stage, the detected cracks were scrutinised in detail to quantify the crack width. [PERSON] et al. [154] pinpointed that generalising infrastructure inspection is equally important despite of the enormous data available on measuring cracks in concrete using deep learning methods; only some models can be used without re-training. So, this study was executed with the help of step transfer learning (STL) and extreme learning machines (ELMs) to create an automated mechanism to figure out surface cracks in concrete infrastructure. From a diverse set of source images, STL can meaningfully generalise abstract features while ELM enables the proposed model to overlook the limitations of the optimisation of conventional ANNs.