# Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11
## 2 Literature Review  
Detection of faults in infrastructure has become very crucial for ensuring the safety and durability of civil structures. While traditional methods have made important contributions, they often struggle to deliver real-time, accurate, and efficient solutions. Non-destructive defect detection techniques in bridges mainly include electromagnetic and thermal imaging methods. These approaches offer valuable insights into structural integrity without causing damage. However, they cannot be used for dynamic scenarios because they lack real-time capability and rely highly on manual intervention [13]. Most vision-based sewer fault detection methods rely on fast R-CNN, which is a deep learning model used for object detection that generates regional proposals before classification, and SSD, which is also a deep learning-based object detection framework known for its speed and efficiency. While these models show potential, they have some limitations regarding detection speed and scalability for analyzing defects. Moreover, handling small-scale defects was a big challenge that reduced their overall efficiency [14]. AI and image processing in SHM have explored various techniques, such as texture analysis and drone-based imaging. These have improved the accuracy of detecting damage; however, they are not as adaptive and capable of real-time processing as the YOLO models; hence, their application to large-scale monitoring is very restricted [15]. Traditional deep learning-based concrete crack detection methods showed advancements in the detection of structural anomalies. However, due to the absence of real-time processing and lower precision in the case of small objects, those techniques were not effective in dynamic environments [16]. AI integrated with sensors for road monitoring methods demonstrated the potential of detecting anomalies like cracks and potholes. However, the lack of usage of YOLO models resulted in slower detection speed and higher computational demands, which have limited their large-scale deployment [17]. Anchor-free CNN models for tunnel crack detection had effectively handled small, thin cracks. However, the limited capability of handling complex defect scenarios and the absence of real-time features have reduced their practicality in high-demand applications [18]. Pavement crack detection techniques using machine learning depend on hybrid models such as SSA-SVM. SSA-SVM is a variation of support vector machines designed for structured prediction tasks. While the accuracy is good enough, the computational inefficiency and non-adaptability compared to YOLO-based methods limit wider applications [19]. The LightGBM models were proposed for detecting cracks in concrete with high accuracy by geometric analysis. However, the processing delay and difficulty in managing real-time data made them less suitable for fast-paced monitoring tasks [20]. A deep hierarchical CNN comprising 16 convolutional layers was used for corrosion detection on the aerial images from the Bolte Bridge (a major freeway bridge in Melbourne, Australia, known for its cable-stayed design and role in urban transportation networks) and sky rail areas. The model produced an impressive result in terms of 98.9% global accuracy but needed manual supervision owing to the shortcomings of SegNet. Furthermore, stability issues with CycleGAN and generalization challenges were also noticed [21]. PADENet approached the panoramic images to detect corrosion types with the high mAP of 87.34%, outperforming well-established approaches, such as Fast R-CNN, YOLO, and SSD, detecting a wide variety of surface damage types with robust precision. It outperformed YOLO and SSD while facing difficulties with small defects and complex backgrounds. High computational requirements also remain a limitation [22]. The reviewed methods have several significant limitations. The reviewed methods cannot be applied in real time and therefore cannot be used for real-time tasks such as fault detection.