A Deep-Convolutional-Neural-Network-Based Semi-Supervised Learning Method for Anomaly Crack Detection

Abstract

Crack detection plays a pivotal role in structural health monitoring. Deep convolutional neural networks (DCNN) provide a way to achieve image classification efficiently and accurately due to their powerful image processing ability. In this paper, we propose a semi-supervised learning method based on a DCNN to achieve anomaly crack detection. In the proposed method, the training set for the network only requires a small number of normal (non-crack) images but can achieve high detection accuracy. Moreover, the trained model has strong robustness in the condition of uneven illumination and evident crack difference. The proposed method is applied to the images of walls, bridges and pavements, and the results show that the detection accuracy comes up to 99.48%, 92.31% and 97.57%, respectively. In addition, the features of the neural network can be visualized to describe its working principle. This method has great potential in practical engineering applications.