## 4 Al and ML in Bridge Inspection
### YOLO-Based Bridge Inspection Techniques
Hence, the model achieved automatic inspection and provided generalisation ability. Fine-grained crack detection in concrete buildings was conducted by [PERSON] et al. [157]. Due to pseudo-labelling, the model can be effectively used on a limited data size. This model allows real-time implementation because of the integration of quantised YOLOv8 models. The integration deployed in this study provides good crack detection performance, mitigates overfitting, and reduces inference time. Three different YOLOv8 models were used, and each model exhibited promising results. During the training phase, YOLOv8x exhibited maximum precision, recall, and mAP values. Three-fourths of the images were used to train the three YOLOv8 models, and the leftover images were deployed as inference data to evaluate each model's performance. The training dataset images were subdivided into three subsets for three models. The incongruuous and complex background did not present any bottlenecks when working on the proposed models. A multi-apparent detection of damage on concrete bridges based on YOLOR was proposed by [PERSON] et al. [158]. A concrete bridge defect (CBD) dataset comprising more than 7000 images was chosen to distinguish as many as nine bridge defects, including cavity, \begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Objective** & **Technique** & **Dataset** & **Outcome** & **Features/Limitations** \\ \hline \multirow{4}{*}{[161]} & \multirow{4}{*}{Identified cracks in concrete bridges.} & \multirow{4}{*}{YOLOv5-TS with the following output features-mAPeV0.5 (0.752), mAPeV0.505 (0.518), and recall (0.794)} & \multirow{4}{*}{ZU SYG [162],} & Four types of mechanical cracks were distinguished & Twin benefits were achieved by using transposed \\  & & & & \begin{tabular}{c} were distinguished \\ viz. horizontal cracks \\ (0°-20°), low-angle \\ cracks (20°-45°), \\ vertical cracks \\ (0°-30°), and \\ high-angle cracks \\ (45°-70°). \\ \end{tabular} & \
\begin{tabular}{c} Twin benefits were achieved by using transposed \\ convolution-enhanced network \\ capacity that enabled it to \\ self-learn weights and minimise \\ characteristic information loss. \\ (0°-30°), and \\ self-learn weights instead \\ (0°-20°), and \\ self-learn weights instead \\ (0°-30°), and \\ self-learn weights instead \\ (45°-70°). \\ \end{tabular} &
\begin{tabular}{c} A generalised investigation takes care of detecting cracks across various domains and backgrounds. \\ backgrounds. \\ The integration of UNet with Gabor filters and CBAM \\ increases the model’s \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\)single crack, pockmark, reticular crack, rust expansion, leakage, whitening, spalling, and exposed rebar. Using the CBD dataset, comparison experiments on performance were conducted among YOLOv3, YOLOv4, YOLO-640, and YOLO-1280 to finally choose the best model. A model called YOLOR-BDIM was developed, and it was selected after taking care of defects in bridges, choosing suitable anchor boxes, and localising loss function. [PERSON] et al. [159] conducted a Deep Learning analysis for multi-damage identification in high-resolution concrete bridge component imagery. For efficient and better learning of damage features by the network, the high-resolution images are converted into sub-images using the help of the Auto-adaptive Window Cropping Method (AWCM) proposed in this study. The advantage of such a cropping method is that cropped images justify their label information, and damaged features are protected from destruction during cropping. A balance in the volume of different types of damage had to be maintained to prevent overfitting. This balanced dataset can be applied to train the DL network and prepare a multi-damage identification model. The restoration of sub-images into their actual high-resolution image and prediction boxes of damage can be achieved after identification.