# Pavement Disease Visual Detection by Structure Perception and Feature Attention Network
## 4 Experiments
### Results Analysis  
#### 4.4.1 Comparison with Different Methods  
Since the proposed SPFAN generally follows the architecture design of YOLO, it is firstly compared with multiple representative versions of YOLO networks. Figure 9 illustrates the pavement diseases detection performance of different mainstream standard \begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Environment** & **Detailed Information** \\ \hline CPU (Central Processing Unit) & Intel(R) Xeon(R) Silver 4316 CPU @ 2.30 GHz \\ GPU (Graphics Processing Unit) & GeForce RTX 3090 \\ CUDA (Compute Unified Device Architecture) & 12.0 \\ Python version & 3.9.19 \\ System version & Rocky Linux 9.2 distribution \\ Deep learning framework & Pytorch \\ Pytorch version & 2.2.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configuration of the experimental environment. Figure 8: Block diagram of the proposed methodology for pavement damage detection. object detection models for the same images. Table 3 display the statistical results of the SPFAN with other object detection models. First of all, it can be observed from Table 3 that almost all the methods are able to obtain satisfactory performance in detecting pavement potholes. A detection accuracy of nearly 80% was achieved, while our proposed SPFAN model achieved an accuracy of 88%. The reason for this is relatively intuitive. Compared to other types of pavement diseases, potholes usually have more distinct visual appearances, which actually simplifies the detection task to some extent. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**[EMAIL_ADDRESS]**} & \multirow{2}{*}{**[EMAIL_ADDRESS]**} & \multirow{2}{*}{**[EMAIL_ADDRESS]**} & \multirow{2}{*}{**GFLOPs**} \\ \cline{2-2} \cline{5-8}  & **Longitudinal** & & & & & & & \\
**Crack** & & & & & & & \\ \hline YOLOv3 & 0.745 & 0.714 & 0.415 & 0.469 & 0.783 & 0.872 & 0.666 & 154.6 \\ YOLOv5n & 0.726 & 0.661 & 0.429 & 0.453 & 0.719 & 0.812 & 0.633 & 4.2 \\ YOLOv6n & 0.778 & 0.76 & 0.447 & 0.478 & 0.787 & 0.784 & 0.673 & 11.4 \\ YOLOv7 & 0.711 & 0.73 & 0.48 & 0.506 & 0.807 & 0.772 & 0.668 & 103.2 \\ YOLOv8n & 0.788 & 0.718 & 0.498 & 0.42 & 0.786 & 0.763 & 0.662 & 8.1 \\ YOLOv9 & 0.762 & 0.763 & 0.472 & 0.414 & 0.796 & 0.776 & 0.664 & 263.9 \\
**SPFAN** & 0.801 & 0.733 & 0.489 & 0.586 & 0.786 & 0.88 & **0.712** & **8.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of defect detection performance and computational cost across different object detection models. Figure 9: Visualized pavement distress detection of comparative models. Second, with the continuous development of CNN, the performance of these models in detecting pavement diseases has not risen consistently. In other words, there is no significant performance difference among these YOLO networks for pavement disease visual detection. The detection accuracy of the original models of YOLO for road defects is roughly 66% [EMAIL_ADDRESS]. This is mainly because that different network architectures are improved with various modules, which are not necessarily suitable for the task of pavement disease visual detection. Finally, compared with other methods, YOLOv8n indeed achieves a good balance between efficacy and efficiency, keeping an acceptable GFLOPs without sacrificing detection performance. While maintaining a lightweight characteristic similar to YOLOv8n, the proposed SPFAN significantly enhances detection performance, achieving a [EMAIL_ADDRESS] of 0.712, which is higher than that of all comparison methods (as shown in Figure 10 and Table 4). More specifically, SPFAN demonstrates superior performance in detecting most pavement distress types, such as longitudinal cracks (highest [EMAIL_ADDRESS] of 0.801), potholes (highest [EMAIL_ADDRESS] of 0.88) and oblique cracks ([EMAIL_ADDRESS] of 0.586), surpassing YOLOv8n and other models in nearly all categories except alligator cracks. This fully proves the effectiveness of the proposed model.