# RDD-YOLO: Road Damage Detection Algorithm Based on Improved You Only Look Once Version 8
## 4 Experimental Results and Analysis
### Experimental Implementation and Experimental Results  
In pursuit of higher accuracy, this paper adopts YOLOv8x as the baseline and conducts comparative experiments using RDD-YOLO and other classic YOLO algorithms, with 180 rounds of training. Figure 13 displays the mAP50 and mAP50-95 curves on the validation set. We can observe that the performance of YOLOv5x and YOLOv7x are comparable, and the performance of YOLOv8x is relatively better. Compared with other YOLO algorithms,  
Figure 12: IoU calculation diagram. the proposed RDD-YOLO model has a faster convergence speed and higher values of mAP50 and mAP50-95. The final mAP50 and mAP50-95 of RDD-YOLO and YOLOv8x on the validation set for different road damage categories can be seen in Table 1. According to the data in the table, both RDD-YOLO and YOLOv8x exhibit the best detection performance for the D20 category, attributed to the distinct features of grid cracks. However, due to the irregular shape, small volume, and relatively fewer samples of potholes, the model's detection performance for D40 is relatively lower. RDD-YOLO achieved final mAP50 and mAP50-95 scores of 62.8% and 36.7%, respectively, showing improvements of 2.6% and 5.2% over YOLOv8x. Specifically, there is a significant improvement in D40 detection, with an increase of 4.5% in mAP50 and 6.2% in mAP50-95. The model also exhibited notable improvements in detecting other types of road damage. RDD-YOLO enhances its ability to retain image details and focus on key information, leading to improved performance in road damage detection tasks. For validating the performance of the algorithm presented in this paper, we compare RDD-YOLO with high-accuracy models such as YOLOv5x, YOLOv7x, and YOLOv8x proposed in recent years. Additionally, we introduce p-YOLOv5x for comparison, a model that uses pre-trained weights from the winning solution in the Global Road Damage Detection Challenge (GRDDC'2020) [32] proposed by [PERSON] et al. [33]. As illustrated in Table 2, the comparison reveals that higher versions of YOLO models have a smaller parameter count compared to lower versions, and the YOLOv8x model outperforms lower versions in terms of performance. The p-YOLOv5x, utilizing pre-trained weights from the winning solution in GRDDC'2020, performs well on the Indian and Japanese datasets due \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Type** & **mAP50** & **mAP50-95** \\ \hline \multirow{4}{*}{YOLOv8x} & D00 & 59.3 & 33.3 \\  & D10 & 59.7 & 30.1 \\  & D20 & 67.7 & 37.7 \\  & D40 & 52.9 & 23.9 \\  & ALL & 59.9 & 31.2 \\ \hline \multirow{4}{*}{RDD-YOLO} & D00 & 62.8 (+3.5) & 38.8 (+5.5) \\  & D10 & 61.4 (+1.7) & 35.2 (+5.1) \\ \cline{1-1}  & D20 & 69.2 (+1.5) & 42.3 (+4.6) \\ \cline{1-1}  & D40 & 57.4 (+4.5) & 30.1 (+6.2) \\ \cline{1-1}  & ALL & 62.5 (+2.6) & 36.4 (+5.2) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of road damage detection results for various types of roads in validation set. Figure 13: mAP50 and mAP50-95 curves of different YOLO algorithms in validation set. to the use of RDD2020 [34], which includes images only from Japan, India, and the Czech Republic. However, its overall F1 score is still lower compared to YOLOv5x. Notably, the proposed RDD-YOLO performs excellently on both overall and individual country datasets, exhibiting superior performance and having a smaller parameter count. Although RDD-YOLO has a smaller number of GFLOPS compared to YOLOv8x, it is still larger than other models, possibly requiring more computational resources and time. In summary, RDD-YOLO excels in both performance and model parameters, surpassing other models. This research result provides strong support for future studies and practical applications.