# Adaptive Attention-Enhanced Yolo for Wall Crack Detection
## 4 Performance Evaluation
### Ablation Study  
In order to demonstrate the improvement of the model performance by the new modules built in this experiment, we conducted an ablation study, which is still based on the original dataset. The baseline for the ablation study was Yolov5s, and each new module was gradually added in during the experiment. We chose to visualize the impact of STC3, ARF, and MHA on the detection of wall cracks through the comparison of mAP, a comprehensive index. The results of the model performance comparison in the experiment are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Params (M)** & **FLOPs (G)** \\ \hline Yolov5 & 82.84\% & 78.21\% & 7.1 & 16.5 \\ Yolov8 & 84.55\% & 79.78\% & 11.2 & 28.6 \\ Yolov10 & 82.60\% & 76.89\% & 7.2 & 21.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of Yolov5, Yolov8, and Yolov10 on crack detection tasks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **STC3** & **ARF** & **MHA** & **mAP** \\ \hline baseline & & & & 83.6\% \\ STC3 & ✓ & & & 84.4\% \\ ARF & & ✓ & & 84.0\% \\ MHA & & & ✓ & 84.0\% \\ STC3 + ARF & ✓ & ✓ & & 85.2\% \\ STC3 + MHA & ✓ & & ✓ & 85.5\% \\ ARF + MHA & & ✓ & ✓ & 84.7\% \\ Ours AAEY & ✓ & ✓ & ✓ & 86.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study results for crack detection (the ✓ symbol indicates the inclusion of the specific module in the model configuration).  
Figure 3: Confusion matrices of Yolov5 **(top left)**, Yolov8 **(top right)**, and Yolov10 **(bottom)**.  
According to the results shown in the table, the baseline mAP value is only 83.6%. When only the STC3 structure is introduced, the model's mAP rises to 84.4%. Similarly, the introduction of only the ARF or MHA structure bring the mAP for each to 84.0%. Adding both STC3 and ARF modules together increases the mAP by 1.6%. Using STC3 and MHA together increases the mAP by 1.9%. Introducing both ARF and MHA together increases the mAP by 1.1%. And when the three new modules are embedded together in Yolov5, the performance of the model in terms of mAP improves even better, with an increase of 2.8%. After analyzing the results of the data from the ablation study, it is clear that all three modules, STC3, ARF, and MHA, contribute more to the task of recognition of cracks.  
Figure 4 demonstrates the effectiveness of the ablation experiment's predictions in the crack detection task, visualizing the change in model performance with the sequential addition of the STC3, ARF, and MHA structures. By scrutinizing the results of this set of experiments, we can clearly see that the detection of the baseline is poor and the identification of cracks is incomplete. In the process of adding new modules, the detection was improved to varying degrees and was able to gradually frame out more areas where cracks were located. When integrating all modules together into Yolov5, the detection results reached a high level and the confidence level improved even more. This result demonstrates that incorporating STC3, ARF, and MHA into YOLOv5 greatly improves the model's crack detection capabilities.