A Deep Learning Paradigm for Railway Bridge Assessment with CNNs

Abstract: The key issue for the railway department has been to examine and monitor railway bridges, as urbanization expands, the availability of railways grows, and the railway system has greatly expanded throughout the nation. The expense of maintaining railroad bridges and associated costs with personnel have been a burden on the railroads. To ensure transportation safety, concrete bridge crack detection is critical. Deep learning technology has made it possible to automatically and accurately detect faults in bridges. The present methods are not accurate and they require a large size of dataset for model training and they require a high computational power model training. The proposed model is a convolutional neural network (CNN) based end-to-end crack detection model. The proposed model achieved a 95% detection accuracy.

Keywords: Deep Learning, CNN, Remote sensing, OpenCV, Keras, TensorFlow.