## 4 Discussion  
This paper proposed a method to address the multi-target and multi-class detection of cracks with complicated background by developing an intelligent model that improves the YOLOv5 network from four aspects and satisfies the requirements for multi-scale crack detection. In addition to the image processing method, prior literature has also attempted to detect and identify pavement distress utilizing methods such as 3D ground penetrating radar, 3D laser scanning, UAVs, etc. Table 6 provides a comparative analysis of related research on these methods from various perspectives. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Detection Object** & **Accuracy** & **Systematic Error** & **Cost** \\ \hline Image processing & Transverse, longitudinal, & & & Missed detection rate: \\ (the proposed method) & block, and alligator & & 2.20\% & Hundreds of dollars \\  & Rainwater wells, cables, & & & \\
3D GPR [53] & pipes, steel mesh, and & & & \\  & cavities & & & \\
3D laser scanning [54] & Potholes & / & Mean error: 1.5–2.8 cm & Hundreds of thousand \\ UAVs [55] & Cavities and bulges & / & & 1 cm \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of pavement distress detection methods. Figure 10: Examples of the experimental results: (**a**) Longitudinal cracks with accurate bounding boxes; (**b**) Transverse cracks with accurate bounding boxes; (**c**) Alligator crack with an inaccurate bounding box; (**d**) Block crack with an inaccurate bounding box; (**e**) Transverse crack detected as a longitudinal crack. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Indicators** & **Transverse** & **Longitudinal** & **Block** & **Alligator** \\ \hline Missed detection rate & 5.26\% & 4.05\% & 5.56\% & 0\% \\ False detection rate & 0\% & 0.12\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of the proposed model on the validation set. All four methods listed in the table are highly efficient and productive. Image processing is commonly utilized to detect visible pavement distress like cracks, potholes, and ruttings while 3D GPR is typically employed for detecting structural pavement distress such as cavities, loose areas, and other underground facilities [56]. The mAPs of image processing and 3D GPR are basically over 80%. UAVs and 3D laser scanning are often used for the detection of distress types like potholes and road safety evaluation by integrating three-dimensional point cloud and images, and they can also measure distress with a margin of error at the centimeter level. In terms of cost, 3D laser scanning and 3D GPR are more costly, UAVs cost a moderate amount, and image processing costs the least. It can be seen from the comprehensive comparison that image processing has major advantages in detecting pavement visible distress due to its economy and feasibility of operations. Furthermore, with the wide application of image processing, people have also carried out a lot of studies. For example, [PERSON] [57] designed a rural pavement distress detection network called crack convolution (CrackYOLO), while [PERSON] [58] investigated crack detection and carried out a comparison study based on Faster R-CNN and Mask R-CNN, and [PERSON] [59] integrated the coordinate attention (CA) module into the backbone of YOLOv5. The datasets they used were collected parallel to the pavement and did not classify cracks; unlike these studies, this study built a dataset of pavement cracks with complicated backgrounds to realize the multi-target and multi-class detection of cracks in complicated scenarios. There are also some studies which have attempted to detect cracks in complicated scenarios, but they focused on different pavement distress types, like Ref. [34] which classified pavement distress types into nine groups, while Refs. [60; 61] classified pavement distress into longitudinal cracks, transverse cracks, alligator cracks, and potholes.