Assessing the Impact of Deep Learning on Grey Urban Infrastructure Systems: A Comprehensive Review

Abstract

This in-depth review scrutinizes recent advancements in the application of deep learning techniques for defect detection across various types of grey infrastructure, including buildings, roads, and bridges. The analysis encompasses a range of studies that employ different convolutional neural network architectures such as Mask R-CNN, Faster R-CNN, U-Net, and YOLO models, tailored to address specific challenges associated with the accurate and efficient detection of structural defects. The datasets analyzed in these studies vary from large-scale, manually labeled images to synthetically generated data, highlighting the adaptability and scalability of these approaches to diverse and complex environments. Performance evaluations are rigorously discussed, focusing on metrics such as mean Average Precision (mAP), accuracy, precision, recall, F1-score, and Intersection over Union (IoU), which are crucial for assessing the effectiveness of the models. Innovations such as the integration of 3D LiDAR data, real-time processing capabilities, and the development of models for challenging scenarios like small, densely packed defect detection are highlighted. This review underscores the significant potential of deep learning in transforming infrastructure maintenance and management, promising enhanced safety, reduced costs, and improved operational efficiency.