## 3 Bridge Inspection
### Structural Health Monitoring
The one-stage model does not incorporate a Region Proposal Network (RPN) and does not deliver end-to-end object detection, which is why they are fast. Still, the level of precision achieved is limited. The two-stage model contains architectures like Faster R-CNN [94] and Spatial Pyramid Pooling Network, SPP-NET [95]. These models' object region detection networks are trained as per the RPN [96]. The two-stage models are slower, but the degree of precision achieved is generally higher than that of the one-stage counterparts. Object detection capacity depends on the accuracy and speed of the algorithms applied [97]. YOLOv5 is an efficient object detection example, giving excellent detection accuracy and inference speed compared to YOLOv3 and YOLOv4. YOLOv5 exhibited a mean average precision (mAP) enhancement of 5% to 16% compared to YOLOv4 across diverse evaluation settings utilising the Common Object in Context (COCO) dataset. YOLOv5 can process the data at 38.4 Frames Per Second (FPS), in contrast to YOLOv4's 23.5 FPS, signifying a 63% enhancement [98]. There can be two methods for damage detection in infrastructure. The first method is destructive models that deploy the removal of structural sample masses for damage assessment. Another is NDT, which can predict the presence or absence of damage without any impact on the structure [99]. The signal-based techniques are built on the latest innovative computing algorithms and sensors [100]. Three signal-based techniques are reported in the literature, viz. time domain techniques [101; 102], frequency domain techniques [103; 104], and time-frequency domain techniques [105; 106]. Under the class of frequency domain methods, there are several NDT, including natural frequency, model curvature [107; 108], frequency response function [109; 110], power spectral density [111; 112], and model strain energy [107; 113]. Among these, power spectral density finds wide application in damage detection, where a sensitive non-linear function of structural factors is used to create a transfer function of the second order [114]. Structural damage identification is achieved using the model update approach. Increasing the frequency points utilised for damage identification can diminish the selection of measurement sites. The results are derived from modifying the impaired parameters utilising the sensitivity of power spectral density. It not only diminishes the size of the analytical model required for model updating but also reduces the uncertain parameters necessitating updates, facilitating a swift convergence of the iterative process [111]. Several recent studies have been conducted based on this power spectral density technique, such as [PERSON] et al. [112], which evaluated corrosion-induced damage on coastal bridges. [PERSON] et al. [115] conducted failure detection tests in concrete bridges using least squares. A Spanish study [116] examined the environmental impact of chloride on a coastal reinforced concrete bridge (Arosa, Spanish region of Galicia) using the power spectral density method. The type chosen was a reinforced concrete bridge constantly eroded by chloride ions. According to this study, chloride corrosion can be subdivided into different levels. The partial spectral density method was compared with conventional techniques. It was concluded that the method applied could diminish the probable harmful effects on the environment by around 14% in the deck and by around 30% in the columns of the bridge in the state of bridge repair and maintenance. In the initial phase, the quantity of chloride ions is below the chloride threshold. Then follows the propagation phase, which includes corrosion followed by structure cover cracking leading to the serviceability state and, finally, the limit state [117]. Table 2 shows a few region-specific case studies using these techniques in crack detection and SHM of concrete bridges.