Deep Learning for Image-Based Monitoring of Transportation Infrastructure: A Review

Abstract

The durability and security of the transport system plays a critical role in economic development, mobility, and the well-being of the population. Prior inspection of roads, bridges and railways has been largely laborious, time consuming and subjective. Recent developments in computer vision and deep learning (DL) open the possibility of automating the monitoring process, improving the accuracy, and facilitating the predictive maintenance. With the help of the DL models, image-based monitoring helps to detect cracks, deformations, corrosion, and structural defects with high precision. The paper provides the overall review of deep learning application in monitoring transportation infrastructure through images. The contributions made by convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and attention-based architectures are discussed in regard to their roles in automated inspection systems. Some of its applications are pavement crack detection, bridge surface inspection, railway track inspection, and tunnel inspection. As demonstrated in the review, DL performs more favourably in comparison to more traditional image processing techniques, particularly with regard to precision, extensibility, and resistance to real-world factors. The main challenges are: small labelled datasets, high computing expenses, scaling to new environments and interpreting models. However, three potentials are capable of being considered in addition to these restrictions: a hybrid approach, transfer learning, and federated learning. The paper also describes ethical, practical, and technological limitations related to the implementation of DL systems to monitor critical infrastructure. The review finds that DL-enabled image-based monitoring is a paradigm shift to smart and sustainable transportation infrastructure management. The application of dynamically executing DL systems in real time, unmanned aerial vehicles (UAVs), explainable AI, and cross-modal data fusion to enhance predictive performance are some of the research directions of the future.