# Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11
## 4 Results
### Analysis of Results  
We use image analysis to detect visible structural problems without explicitly modeling the deterioration process. The method is intended to complement current inspection techniques instead of replacing predictive maintenance models. We recognize the value of incorporating deterioration insights to enhance accuracy and intend to explore this in future research. The YOLOv11-based training graphs are displayed alongside YOLOv10 and YOLOv12 graphs in Figures 7-9, showing that the best results are obtained at training step 99 for the proposed scheme. Thus, the decision to train for 100 epochs is based on the observed performance, the early stopping mechanism, and the best results found in 99 iterations. Furthermore, the proposed YOLOv11 exhibits a recall of 35%, a precision value of 39%, and a mAP of 30% when trained with 100 epochs. With our own dataset, the proposed YOLOv11-based model object detection is tested and compared with other models such as YOLOv8, YOLOv9, YOLOv10, and YOLOv12. The YOLOv11 model is proposed because of its better parameter results, which are relatively good compared to the rest. Table 4 provides the details of different models' performances for all classes with different iteration steps. The detector evaluates performance by tracking the [EMAIL_ADDRESS] (mean average precision) during the training phase when it learns on the validation set. Here, a greater value indicates better learning. Then, the F1 score is calculated from the equation  
\[\text{F1 score}=\frac{(2\times\text{Precision}\times\text{Recall})}{(\text{ Precision}+\text{Recall})}\]  
The YOLOv11-based model shows superior accuracy in both the F1 score and [EMAIL_ADDRESS], with 37% and 30%, respectively. Table 4 displays the model complexities for all models. The precision, recall, [EMAIL_ADDRESS], and F1 score is quite lower than standard benchmarks due to the complexities inherent in disaster-related imagery. In such scenarios, various types of infrastructure defects can appear within a single image, requiring multiple class annotations. Our dataset includes numerous examples where multiple defects are present in a single image or where the image is heavily zoomed in or blurred out, necessitating annotation of the entire image as damaged/defective. Thus, this results in significantly lower metrics values. On the contrary, the results of the proposed YOLOv11 are relatively higher than those of other models, which are displayed in Table 4. The YOLOv11 model is the most generalization-capable among all models due to its high number of trainable parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Epoch** & **Class** &
\begin{tabular}{c} **Trainable** \\ **Parameters** \\ \end{tabular} & **F1 score** & **[EMAIL_ADDRESS]** \\ \hline Proposed & 100 & All & 9.46 M & 0.37 & 0.30 \\ YOLOv11 & 50 & All & 9.46 M & 0.35 & 0.29 \\ YOLOv11 & 100 & All & 11.17 M & 0.35 & 0.28 \\ YOLOv8 [32] & 50 & All & 11.17 M & 0.35 & 0.28 \\ YOLOv9 [33] & 100 & All & 1.98 M & 0.32 & 0.27 \\ YOLOv9 [33] & 50 & All & 1.98 M & 0.33 & 0.29 \\ YOLOv10 [33] & 100 & All & 2.73 M & 0.31 & 0.26 \\ YOLOv10 [33] & 50 & All & 2.73 M & 0.34 & 0.27 \\ YOLOv12 & 100 & All & 2.58 M & 0.30 & 0.21 \\ YOLOv12 & 50 & All & 2.58 M & 0.25 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Testing performance of YOLOv11, YOLOv8, YOLOv9, YOLOv10, and YOLOv12. Figure 9: YOLOv10-based training results graph with 100 epochs. Additionally, the accuracy of prediction is quite high in the confusion matrix diagram that is presented in Figure 10 for 100 epochs. The confusion matrix has rows and columns where a row represents an actual class, and columns represent a predicted class. The Cracked-Damaged-Sidewalks class holds the highest prediction accuracy, which is 93%. On the other hand, the Fire-Damaged class has a 7% prediction accuracy, which is the lowest of the bunch.