# Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11
## 5 Conclusions  
In this study, the YOLOv11 model is proposed for detecting infrastructure faults in diverse environments. The absence of an explicit deterioration model is one of the limitations of our approach. Our method can identify visual defects successfully, but incorporating engineering knowledge on infrastructure aging could lead to better long-term predictive performance. Future research may use historical degradation data or physics-based models to enhance robustness. GANs and diffusion models, among other generative AI techniques, can be used to synthesize high-quality training data, which could enhance fault detection models, especially in scenarios wherein real-world labeled data are scarce. Furthermore, Causal AI can help analyze not just correlations but also the underlying causes of structural faults, enabling more interpretable and reliable predictions  
This work focuses on improving the efficiency and reliability of automated fault detection systems by accurately identifying and classifying various types of infrastructure defects across diverse environments. A custom dataset with 9116 images was created, covering a diverse range of fault types and environmental conditions. This dataset strengthens the model's generalizability compared to previous studies. The custom dataset was pre-processed using annotation techniques and data augmentation methods to improve the model's performance and robustness across varying environments. Each image was resized and annotated meticulously to ensure high-quality input for the model. The YOLOv11 model was trained for 100 epochs, achieving impressive results with a recall of 35%, precision of 39%, an F1 score of 37%, and mean average precision (mAP) of 30%. The detection results show that the YOLOv11 model works better than other baseline algorithms (YOLOv8, YOLOv9, and YOLOv10). The findings highlight the model's robustness and efficiency in detecting various fault types in infrastructure, providing a reliable tool for real-time infrastructure fault detection across various applications. The research contributes to the advancement of automated detection techniques, offering the potential for practical deployment in infrastructure maintenance and safety monitoring. This study is limited by the dataset, which includes only 21 structural faults, and may not cover all possible real-world fault scenarios. Variations in image quality, multiple classes in a single image, lighting, and complex backgrounds could also impact model performance. Future work will focus on expanding the dataset to include more diverse fault types and improving the model's adaptability to various real-world conditions. We acknowledge that, despite the exploration of a 2D image-based approach as a tool for infrastructure assessment, it cannot completely replace manual visual inspections at this time. The effectiveness of manual inspections lies in their ability to provide spatial context, depth perception, and evaluate subtle or hidden defects that 2D images may not be able to capture. To obtain high-quality photographs for analysis, it often requires the same level of access and effort as manual inspection. The limitation indicates that our current method is intended to be a complementary tool rather than a complete replacement for manual inspection. More advanced imaging technologies--such as photogrammetry, LiDAR, or other adaptive 3D imaging methods--can provide advanced spatial information and potentially improve the accuracy and reliability of automated defect detection systems in the future. Additionally, integrating multi-modal data could enhance detection accuracy and reliability in challenging environments. Conceptualization, R.Z.R., K.F.B., and S.R. ; methodology, R.Z.R. and S.R. ; software, R.Z.R. and M.R. ; validation, R.Z.R., M.R., and S.R. ; formal analysis, S.R., J.U., and F.A.F. ; investigation, S.R. and J.U. ; resources, S.R. ; data curation, R.Z.R. and M.R. ; writing--original draft preparation, R.Z.R., M.R., and K.F.B.