# 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
To further validate the effectiveness of the introduced improvements on the RDD-YOLO model, a series of ablation experiments were conducted, as shown in Table 3. By comparing the experimental results, it can be found that the introduced improvements have a positive impact on model performance while almost not affecting the model size and inference speed. First of all, based on the experimental results, it can be discovered that the SimAM and the replaced bilinear interpolation in this paper improved the F1 score of the original model by 1.5 and 0.6 percentage points, respectively. This indicates that by introducing these improvements into the model structure, we can significantly enhance the model's detection capability for road damage, and these improvements do not increase the model's complexity and computational overhead. Secondly, the GhostConv structure introduced in this paper not only improved the F1 score by 0.3% but also reduced a certain amount of model parameters and computations. In the end, considering all improvements, the proposed method in this paper achieved a 2.8% increase in F1 score in contrast with the baseline model YOLOv8x. While ensuring detection accuracy, there was also a certain degree of reduction in model parameters and computations. Figure 14 shows the detection performance of our model on the test set for road damage. Different subplots correspond to four common types of road damage mentioned above. Through the observation of these subplots, we can deduce some important features of the detection results. Firstly, for alligator cracks and road potholes, due to their relatively distinct features, we can observe high confidence scores for the predicted boxes. This indicates that our model performs well in detecting these more obvious damage types and accurately marks their positions. However, longitudinal cracks and transverse cracks are usually characterized as very small and narrow. Due to the more detailed morphology of these cracks, the model tends to exhibit relatively lower confidence during detection. Nevertheless, the model in this paper still shows satisfactory performance, being able to locate and fit these cracks with more elongated morphology more accurately. Overall, our \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **F1 (India)** & **F1 (Japan)** & **F1 (United States)** & **F1 (6 Countries)** & **Params/M** & **GFLOPs** \\ \hline YOLOv5x & 44.9 & 63.5 & 68.0 & 63.6 & 88.5 & 220.5 \\ P- & 46.8 & 64.5 & 63.2 & 60.0 & 88.5 & 220.5 \\ YOLOv5x & 42.1 & 62.0 & 68.1 & 62.6 & 70.8 & 188.9 \\ YOLOv8x & 43.6 & 66.3 & 71.5 & 66.8 & 68.2 & 258.1 \\ RDD- & 53.0 & 70.2 & 73.6 & 69.6 & 65.9 & 255.3 \\ YOLO & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results of different algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **SimAM** & **GhostConv** & **Bilinear** & **F1** & **Params/M** & **GFLOPs** \\ \hline YOLOv8x & & & & 66.8 & 68.2 & 258.1 \\ YOLOv8-S & \(\surd\) & & & 68.3 & 68.2 & 258.1 \\ YOLOv8-G & & \(\surd\) & & 67.1 & 65.9 & 255.2 \\ YOLOv8-B & & & \(\surd\) & 67.4 & 68.2 & 258.1 \\ RDD-YOLO & \(\surd\) & \(\surd\) & \(\surd\) & 69.6 & 65.9 & 255.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model improvement ablation experiment. model performs well with different shapes of cracks and shows good generalization. Our model is not only able to adapt to the more obvious and common types of damage but is also able to deal with the more detailed cracks, providing a reliable solution for the comprehensive detection of road damage. This is of positive significance for ensuring road safety.