# Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11
## 1 Introduction
This model detects cracks in real time with greater accuracy and speed. YOLOv9 accomplished the most elevated exactness, with a precision pace of 92.1% and a handling rate of 1.051 h per model preparation [6]. BFD-YOLO is a superior recognition strategy based on YOLOv7 that is proposed for detecting defects in building facades. YOLOv7 was particularly suitable for this task because of its increased accuracy in detecting minute defects on complex surfaces. This model integrates MobileOne, a lightweight network module, to reduce parameter count and inference time. Over-parameterization and re-parameterization methods will enable the model to grasp more features efficiently, while adding the MobileOne module will reduce the number and complexity of network parameters, hence effectively reducing the estimation time [7]. The environment, human prosperity, and planetary natural frameworks all depend upon water, which is basic until the end of time. The YOLOv8 model has been used in water pipelines to identify problems such as water spills, breaks, and corrosion. The YOLOv8 model has been applied to water pipelines to detect problems like water spills, breaks, and corrosion. It is a technology that enhances the efficiency of detecting faults in the pipeline, thereby reducing actual site investigations and facilitating the maintenance process of the water distribution system. This method ensures early identification of the problems, advances efficacy, and improves the overall management of water systems by automating fault finding [8]. The LCA-YOLOv8n-seg approach is presented for crack identification, which frames cracks and identifies crack regions at the pixel level using an enhanced one-stage instance segmentation model. The ProtoC1 module and a new backbone network, LCANet, enable the model to achieve high accuracy, real-time performance, and decreased size, which makes it appropriate for low-performance devices [9]. ML-YOLO has been upgraded in the algorithm to classify and detect road defects that affect the safety of the road, such as splits, potholes, and bulges. The refined YOLOv8-based approach addresses the limitations of manual inspection, includes a distraction attention module for enhanced feature extraction, and optimizes localization algorithms for precise defect detection and spatial location [10]. Fire and smoke are significant threats that destroy property and the environment. The traditional methods have problems with accuracy and speed, which makes real-time detection difficult. A better approach is developed using the YOLOv8-based deep learning model, which outperforms baseline models (YOLOv7, YOLOv5, ResNet-32, and MobileNet-v2) in precision, recall, and mean average precision metrics [11]. Detecting infrastructure problems has received a significant amount of interest because of YOLO models' ability to detect objects in real time. At the same time, some researchers still face too many obstacles and constraints to overcome the challenge of improving their efficiency. Limited Fault Types Coverage: Most current work is focused on specific problems, like damage to walls or cracks on roads, without considering many other important faults like skyscrapers, pipelines, or structural damage in historical buildings. The limited scope of these models reduces their practical applicability. Underutilization of YOLO Advances: Despite improvements in speed and accuracy of recent YOLO versions such as YOLOv8, YOLOv9, YOLOv10, and YOLOv11, the potential for detection of infrastructure issues remains largely unexplored. Therefore, this area has fallen behind regarding correct defect detection. Inadequate Datasets: Most research projects use datasets that are either too limited, focusing on specific types of infrastructure, not diverse enough, or lacking the right annotations. It is hard to train models that can generalize different kinds of failures. Small or Hidden Faults are Hard to Identify: The present models are not capable of finding minute cracks, hidden damage, and other such problems.