# Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11
## 1 Introduction
There is a need for techniques that will enhance the identification of less evident problems. Limited to Real-World Implementation: Despite the potential, actual applications of YOLO-based models have not been widely used in infrastructure monitoring systems. We should concentrate our research on integrating these models into practical applications for continuous fault detection and safety monitoring. This research is motivated by the increasing need to overcome the difficulties presented by infrastructure monitoring. As infrastructure in urban areas becomes increasingly complex, traditional inspection methods face challenges in meeting the ever-growing demand for efficiency and accuracy. The main objective of this study is to address these challenges by integrating deep learning technology into infrastructure monitoring, especially object detection based on the YOLO model. The research provides innovative approaches towards addressing grave problems in infrastructure management by focusing on safety, cost-effectiveness, and preservation. The inability to locate defects in infrastructure such as roads, bridges, and pipelines may lead to severe problems. This study saves people from accidents and protects them through the early identification of problems, which is important for both urban and rural areas. Most structural problems are expensive maintenance and repair costs. This model can help reduce such costs by detecting defects in their early stages, which saves money and time for business enterprises and governments by increasing the life span of such infrastructures. Most of the methods for defect detection today are tedious and time-consuming. The following research provides a better approach for infrastructure monitoring using the YOLO model to build an automated, faster, more accurate, and scalable system. This research can detect different types of faults in infrastructure, such as potholes, cracks, and structural damage in old structures, whereas conventional techniques can only detect one type of fault. Because of this, it can be applied to many real-life applications. Identifying cracks and other issues is essential for the preservation of ancient buildings. This research helps uncover issues that may remain invisible in the conservation of cultural artifacts, helping to protect these cultural treasures for future generations. The creation of a specialized dataset that includes both historical and modern infrastructure is a significant benefit to the research community. It can be utilized in future studies on machine learning applications and infrastructure problem detection. By identifying flaws early, this research supports more sustainable infrastructure practices by reducing maintenance waste and optimizing resource use. The main contributions of this paper are as follows:  
* This study focuses on detecting faults in infrastructure to improve safety and maintenance by accurately identifying defects. First, we created a custom dataset containing 9116 images covering various fault types and environmental conditions. This extends the generalization capability of the proposed model compared with other works. * Second, we pre-processed the custom dataset using annotation techniques to enhance the model's performance and resilience in a variety of settings. We resized and labeled each image to ensure high-quality input for the training process. With pre-processed images, the proposed deep learning-based object detection YOLOv11 [12] model is trained to precisely detect and categorize different types of faults in real time, significantly improving the detection speed. * Finally, a detailed analysis of the effectiveness of YOLOv11 was conducted, and indeed it is superior to other baseline models, including YOLOv8, YOLOv9, and YOLOv10. Evaluation results prove that, in all instances, the proposed YOLOv11 outperforms others. * This research gives a significant lead in infrastructure fault detection by using a proposed YOLOv11 model and a diverse, high-quality dataset.