# Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11
## 1 Introduction  
Infrastructure has incessantly suffered decay from increasing industrialization and urbanization, a changing climate, globalization, natural calamities, environmental pollution, and transportation overload. Faults in infrastructure--from minor fissures to major structural collapses--endanger public safety, disrupt daily operations, increase repair costs, and have significant economic consequences. Infrastructure faults are defects or breaks in physical structures such as bridges, building walls or surfaces, power grids, potholes, roads, historic buildings, manholes, pipelines, railways, and shipping containers, which disrupt the normal functioning of society by preventing the provision of essential services. Some of the reasons for these flaws include aging equipment, natural disasters, excessive use, shoddy construction, faulty designs, or malfunctioning systems. Defects include things like pits, cracks, corrosion, and structural deformation. Identifying framework faults is noteworthy for public security, preventing accidents, and sparing lives. It helps in economic stability by not disrupting the usual activities and reduces asset costs by findingsolutions early. Generally, this action has until now relied upon sensor-based observing and manual verifications. However, human error, excessive cost, and inefficiency often limit these methods when dealing with large infrastructures. As innovation has progressed, automatic defect detection systems that use machine learning and image processing have become game-changing tools that provide accurate, scalable, precise, versatile, and real-time findings. Detecting infrastructure faults has several advantages. Increased Safety: Timely identification prevents catastrophic failures and ensures the safety of users and the environment. Cost Savings: Early fault identification lowers repair costs by addressing issues before they become more serious. Observing Effectiveness: Automated systems detect defects on a larger scale in less time with less human effort. Long Life: Consistent observation and maintenance develop the strength of infrastructure. Environmental Protection: The early detection of faults helps reduce pipeline leaks and collapses that could have serious environmental repercussions [1; 2; 3]. The accuracy and efficiency of manual inspection techniques for infrastructure flaws is limited. Advanced deep learning models, YOLOv2 and YOLOv3, effectively detect surface defects on bridges, such as corrosion, rebar exposure, and cracks, with faster detection rates than Faster RCNN. The improved YOLOv3 model using both high- and low-resolution feature images outperforms YOLOv2 in detection performance, achieving an AP of 0.91 which refers to a metric used to evaluate object detection models by measuring precision-recall tradeoff compared to YOLOv2's 0.83-0.86. This makes the improved YOLOv3 103 times faster than Faster RCNN (0.23 FPS), with similar precision (0.91 for YOLOv3 versus 0.9 for Faster RCNN). It emphasizes the prospect of deep learning models for real-time and efficient infrastructure monitoring and defect detection [4]. Detecting cracks in railways is important for preventing train derailments and accidents. Detecting railway-track cracks using a deep learning-based method, YOLOv5, for real-time identification and EfficientNet for classification, improves maintenance accuracy and railway safety. The model reaches an accuracy of 91% with a diversified dataset of 1074 images and geospatial localization for precise defect mapping. This integration enhances railway safety through real-time crack detection and focused maintenance, with the potential for further scalability and increased geospatial applications [5]. Through old buildings, we can know the history and culture of a country, which connects people with the past. Old buildings need to be preserved to prevent decay. A deep learning-based method, the YOLO model, versions YOLOv5 to YOLOv10, has been used to detect surface cracks in historic buildings.