# Pavement Disease Visual Detection by Structure Perception and Feature Attention Network
## 4 Experiments
### Results Analysis
#### 4.4.3 Analysis on Feature Attention Module  
The feature attention module is instantiated by CBAM, which is expected to enhance the feature representation capability of the model by suppressing extraneous information in the image. In addition, it is supposed to improve the detection performance by guiding the feature extraction process without significantly increasing the computational cost, which is particularly important for pavement disease detection. In order to systematically verify its effectiveness, a series of experiments are designed and carried out, and the \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **[EMAIL_ADDRESS]** & **GFLOPs** & **Advantages** & **Disadvantages** \\ YOLOv6n & 0.673 & 11.4 & Moderate computational cost; Anchor-free design improves detection speed; Enhanced for repair and potholes. & Weak performance on alligator and oblique cracks. \\ YOLOv7 & 0.668 & 103.2 & Strong detection for repair tasks; Benefits from E-ELAN network for better feature aggregation. & Moderate overall accuracy; Weak on oblique cracks. \\ YOLOv8n & 0.662 & 8.1 & Best detection for alligator cracks; Balanced accuracy and low computational cost. & Worst performance for oblique cracks. \\ YOLOv9 & 0.664 & 263.9 & Good for longitudinal cracks; Advanced feature fusion enhances detection accuracy. & Needs improvement for oblique cracks; High computational cost. \\ SPFAN & 0.712 & 8.1 & Best overall accuracy; Strong on alligator and oblique cracks; Highest for potholes. & Slightly higher computational cost compared to YOLOv5n. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont_. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Network** & **[EMAIL_ADDRESS]** & **F1-Score** & **Params/M** & **GFLOPs** \\ \hline Base network & YOLOv8n & 0.662 & 0.642 & 3.01 & 8.1 \\ \hline \multirow{4}{*}{Insert positions} & +C2f\_DCNv2 (backbone) & 0.671 & 0.672 & 3.04 & 8.0 \\  & +C2f\_DCNv2 & 0.675 & 0.667 & 3.07 & 7.9 \\ \cline{1-1}  & (backbone+neck) & 0.69 & 0.676 & 3.04 & 8.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of inserting C2f_DCNv2 module at different locations. settings and results are listed in Table 6. First, in order to determine the optimal insertion location of the CBAM, we inserted the CBAM at three different positions of the YOLOv8n network and analysed its corresponding impacts on the detection performance. That is, inserting CBAM at the end of neck part, inserting CBAM after three different scales (1/8, 1/16, and 1/32) of feature maps, and inserting CBAM before the SPPF of the backbone network, which are denoted as CBAM (a), CBAM (b), and CBAM (c), respectively. Then, at the identified optimal position, we further replace CBAM with three other mainstream attention mechanisms (CA, Global Attention Mechanism (GAM) [42], and Efficient Channel Attention (ECA) [43]) to validate the enhancement of feature extraction and performance improvement provided by CBAM in terms of its advantages. From Table 6, it can be found that inserting CBAM before the SPPF structure of the backbone, i.e., CBAM (c), maximises the effect of CBAM on the SPFAN and improves [EMAIL_ADDRESS] to 0.683, which is an improvement of 3.17% compared to the original model. Introducing the attention mechanism before SPPF can filter and optimise the features before fusion, effectively suppressing the redundant information and highlighting the key areas, making the input features purer and more targeted. In addition, since the features in the later stages of the backbone network have higher semantic levels, adding the attention mechanism at this time can further enhance the semantic expression, thus significantly improving the fusion quality and detection accuracy of multi-scale features. Then, on the identified optimal position (CBAM (c)), we further replace CBAM with three other mainstream attention mechanisms to validate the enhancement of features and the improvement of performance by CBAM.