# Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11
## 2 Literature Review
Improved YOLOv4, integrated with MobileNetV3 for the detection of railway surface defects, can effectively overcome such difficulties as the small size and complex features of the track. Using a rail image dataset, it achieves significant improvements in accuracy, speed, and parameter efficiency compared to YOLOv3 and Faster R-CNN. This method shows the potential of lightweight and high-performance models in real-time defect detection and points out some directions for further work in dataset expansion and model refinement. However, the small dataset of 1000 images restrict generalization and scalability [23]. YOLOv5 is employed to detect road structure defects, focusing on crack and gap defects in cement and asphalt pavements. The accuracy was as high as 85%, sensitivity reached up to 91.5%, and the F1 Score was 81.5% during its validation, which shows its potential for deep learning regarding efficient pavement fault detection and maintenance. This research was conducted with only 420 images, and therefore a small dataset could limit the model's robustness or scaling in a variable environment [24]. The authors used YOLOv5s to be integrated with IoT data in infrastructures that have been subjected to mining operations for real-time crack detection and machine learning-based concrete strength prediction. It achieved a mAP of 88.41%, and a precision of 87.9%, proving the suitability of the model in real-time structural monitoring. The low recall rate may reduce the detectability of small or low-contrast cracks [25]. Mosaic data augmentation, pruning, and knowledge distillation are incorporated into the improved model of YOLOv5 for detecting structural defects in concrete dams. It realizes a 35% model size reduction with the capability of dealing with complex inspection scenarios effectively. This real-time system is optimized for UAV-captured images, offering high accuracy and efficiency for practical dam inspections [26]. ACD-YOLO is a YOLOv5 model for steel surface defect detection, enhanced by anchor optimization, context augmentation, and efficient convolution modules. The method is industrially applicable for quality assurance because of the balance between speed and accuracy [27]. The research introduces TLMDDNet and its lightweight version, DC-TLMDDNet, to railway track defect detection. It realizes a high mAP of 99.20% with improved features by the enhanced YOLOv3 model and attains an improved detection speed of 34.25 FPS to make defect defection effective and efficient. The research is limited by the dataset to a few defect types and thus may not be generalizable to all railway track conditions [28]. The authors propose an enhanced version of YOLOv5, called MN-YOLOv5, by introducing MobileNetV3 and a lightweight Coordinate Attention module for enhancing road damage detection. The major contributions of this work are a reduction in model size, number of parameters, and GFLOPs, while the mAP increased by 2.5% and F1 score by 2.6%. The lightweight model efficiently detects pavement cracks and helps in timely repairs with less effort during manual inspection [29]. The paper proposes BD-YOLOv8s, an improved YOLOv8s framework that is specially designed for the detection of concrete bridge defects in complicated backgrounds. Key novelties include ODConv for adaptability, CBAM for focusing on critical features, and CARAFE for refined upsampling [30]. It is an improved version of the YOLOv5s model for detecting sewer pipeline defects under the conditions of weak lighting and complicated backgrounds. It aims to improve test accuracy with computational efficiency by incorporating involution modules, GSConv, and CBAM attention mechanisms. It reached a mAP as high as 80.5%, reduced the parameters by 30.1%, and supported real-time detection at 75 FPS, outperforming other models in performance [31]. In contrast, non-YOLO models, like Faster R-CNN or SSD, have high accuracy but sometimes are unfortunately much slower with more computational demands.