Identification of concrete surface damage based on probabilistic deep learning of images

Abstract

Crack is common damage that can reduce the durability of concrete structures and accelerate structural degradation. With intent to improve the accuracy and efficiency of image-based crack detection, deep learning has been extensively used. However, the current deep learning paradigms for this purpose are deterministic machine learning approaches that cannot evaluate the uncertainty of diagnosis results. To bridge this gap, this paper proposes a hybrid probabilistic deep learning method for concrete surface damage classification. In the proposed method, Bayesian inference is embedded in a deep convolutional neural network, and the parameters in probability layers are modified from deterministic values to Gaussian distributions. Experimental results showed that it can identify cracks with an accuracy of 0.9909. The quantitative indicator of uncertainty in the identification results increases with the noise level and the degree of occlusion, which can reflect the ability of the model to capture uncertainty well. In addition, the hybrid probability method based on transfer learning can significantly improve the recognition accuracy, which can reach 0.9249 in complex crack images. Even if the image contains more noise, it can be recognized accurately but the uncertainty indicator is greater than 0.07.