## 4 Results and Discussion
### Comparison Experiments  
In order to prove the validity of the AAL-Net, we compare it with YOLOv3-tiny, YOLOv5s, and YOLOv5s-tiny, which replaces all Convs in YOLOv5s with GhostConv. To prove the robustness of the method, the experiments are carried out on the self-made dataset (A1) and pothole600 dataset (B1), respectively. The results are as follows. As shown in Table 3, in the self-made dataset, the parameters of the AAL-Net are about 58% less than YOLOv3-tiny, the GFLOPs are about 37% less, and the F1 value is increased by 4.35%. The parameters of the AAL-Net are about 47% less than YOLOv5s and about 48% less than GFLOPs, and the F1 value is increased by 2.18%. Even though the parameters of the AAL-Net and GFLOPs are close to YOLOv5s-tiny, the F1 value is 1.11% higher than YOLOv5s-tiny. In addition, to prove the validity of our data augmentation method, we train the AAL-Net in the training set of the fogging and negative sample dataset (A4), test it in the test set of the self-made dataset (A1), and the final result is 13.68% higher than the F1 value train in the training set of the self-made dataset (A1). We visualize the experimental results (Figure 10) and the actual results (Figure 11). Figure 11: Visualization of image detection on the self-made dataset using different methods. Similarly, as shown in Table 4, in the pothole600 dataset, the F1 values of AAL-Net are 6.06%, 1.53%, and 0.42% higher than those of the YOLOv3-tiny, YOLOv5s, and YOLOv5s-tiny models, respectively. With our data augmentation method, the F1 value of the AAL-Net is increased by 0.4%. The experimental results (Figure 12) and the actual effects (Figure 13) are visualized. Figure 10: In the self-made dataset, the comparison between F1 values and parameter values of different methods, the horizontal axis represents different methods, the left half of the vertical axis represents F1 values, and the right half of the vertical axis represents parameter values. By comparing and analyzing the experimental results, the AAL-Net proposed by us has less parameters than the tiny version of YOLOv3, YOLOv5s, and the model replacing \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 (\%)** & **Parameter** & **GFLOPs** \\ \hline YOLOv3-tiny & 91.12 & 88.97 & 90.03 & 8.67M & 13.0 \\ YOLOv5s & 93.11 & 96.06 & 94.56 & 7.02M & 15.9 \\ YOLOv5s-tiny & 95.29 & 96.06 & 95.67 & 3.68M & 8.2 \\ AAL-Net & 95.34 & 96.85 & 96.09 & 3.67M & 8.2 \\ \hline AAL-Net+aug & 95.38 & 97.63 & 96.49 & 3.67M & 8.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of experimental results with existing methods in the pothole600 dataset. Figure 12: In the pothole600 dataset, the comparison between F1 values and parameter values of different methods, the horizontal axis represents different methods, the left half of the vertical axis represents F1 values, and the right half of the vertical axis represents parameter values. Figure 13: Visualization of image detection on the pothole600 dataset using different methods. Conv in YOLOv5s with GhostConv, which indicates that the AAL-Net proposed by us has lower memory requirements for embedded devices and is easier to deploy. The value of the GFLOPs is lower, indicating that the AAL-Net proposed by us is faster and can better meet the real-time detection of potholes. At the same time, the F1 value is higher in the self-made dataset and pothole600 dataset, indicating that the AAL-Net proposed by us has a strong generalization ability and can detect the potholes on the road surface more accurately. In addition, through our data augmentation method, the F1 value of the AAL-Net is further improved, indicating the effectiveness of our data augmentation method. Compared with the model replacing Conv with GhostConv in YOLOv5s, we use the LF module and add the NAM attention mechanism, and the parameter is lower, which indicates the light weight of the LF module we proposed.