Automatic crack distress classification from concrete surface images using a novel deep-width network architecture

Abstract The condition monitoring of concrete surface plays a significant role in civil infrastructure management system. Crack is the main threat to concrete surface of buildings, bridges, roads and pavements. This issue has been researched for several decades, however, it is still a challenge to classify crack since there are many inferior factors, e.g., intense inhomogeneity, structure complexity and background noise of concrete surface. In this paper, a novel deep-width network (DWN) architecture is used for binary and multi-label concrete surface crack classification without handcraft feature extraction. It intelligently learns cracking structures from input raw images by linear and nonlinear mapping process, flexible dynamically updates new weights and efficiently constructs the network by adding new incremental samples. The presented crack distress classification method is tested on two concrete surface crack image datasets and compared with many popular classification methods like sparse autoencoder (SAE), convolution neural network (CNN), and broad learn system (BLS). Experimental results demonstrate that it obviously outperforms those methods both in accuracy and efficiency.