Automated fatigue crack detection in steel box girder of bridges based on ensemble deep neural network

Abstract

This paper constructs a novel ensemble deep neural network to improve identification accuracy of bridge cracks in complex backgrounds. In the three sub-networks of the ensemble network, the detection classifier distinguishes cracks and distractors at the scale of image patches, and then the segmentation sub-network obtains pixel-level crack details. Meanwhile, an innovative crack inference sub-network is constructed based on the prior knowledge of crack morphology. The inference sub-network can predict the probability of cracks existence using the learned image correlation between adjacent regions, and feedback to the detection and segmentation sub-networks. Then, the prediction probabilities coming from the image feature and morphological reasoning are fused to judge and correct the crack repeatedly. Therefore, the presented ensemble deep neural network can well simulate human multi-scale observation, reasoning and decision-making processes. The identification results show that the proposed algorithm can effectively reduce the misjudgment and provide more accurate crack segmentation.