A Review on Deep Learning Techniques for Railway Infrastructure Monitoring

Abstract

In the last decade, thanks to a widespread diffusion of powerful computing machines, artificial intelligence has been attracting the attention of the academic and industrial worlds on itself. This review aims to understand how the scientific community is approaching the use of deep-learning techniques in a particular industrial sector, the railway. This work is an in-depth analysis related to the last years of the way this new technology can try to provide answers even in a field where the primary requirement is to improve the already very high levels of safety. A strategic and constantly evolving field such as the railway sector could not remain extraneous to the use of this new and promising technology. The railway sector offers many aspects which can be investigated with these techniques. This work aims to expose the possible applications of deep learning in the railway sector established on the type of recovered information and the type of algorithms to be used accordingly.

From structure health monitoring to forensics: adapting computer vision to support victims of violence

“…In real-time applications, such as surveying using UAVs or using mobile devices, it is important to address the need for fast and efficient calculation. Among various deep learning models, the most popular CV task is object detection, and ResNet and Faster R-CNN are among the most common methods in infrastructure monitoring [13]. While ResNet and its variants are very popular and powerful, MobileNet and its variants are known as lightweight networks that can tackle the issue of real-time applications [2].…”