Automatic Assessment of Structural Damage of Masonry Structures by Visual Analysis of Surface Cracks

Abstract

Crack detection on the road or building surface is normally done using manual inspection by specialists. The process consumes a lot of time, and the inspection result might differ depending on the specialist’s experience and knowledge. This work will propose an automated detection and rating of cracks on concrete surfaces based on convolutional neural networks (CNNs). Our method also provides a visualization of how the model learns the crack by directing the attention of the model to the different parts of the image by utilizing Gradient-weighted Class Activation Mapping (grad-cam) library. Finally, we show how combining two different data types, such as raw images and manually extracted features, into a hybrid convolutional neural network can increase the accuracy of the model.