Research on Detection Algorithm for Bridge Cracks Based on Deep Learning

The traditional image processing algorithms failed to detect the bridge cracks and the effect was not ideal if the classical deep learning models were used to detect the bridge cracks directly. In order to solve these problems, an algorithm for the detection of bridge cracks based on deep learning was proposed. Firstly, the bridge images with cracks were divided into smaller bridge crack patches and bridge background patches by using the window sliding algorithm. According to the analysis of the patches, a classification model based on convolutional neural network, called DBCC (Deep bridge crack classify), was proposed and the model was used to identify the bridge background patches and bridge crack patches. Secondly, the DBCC classification model combined with an improved window sliding algorithm was used for the detection of bridge cracks. Finally, the algorithm was accelerated by using a search strategy of combining image pyramid and ROI. The experimental results show that the algorithm has better recognition effect and stronger generalization ability compared with the traditional algorithm. Copyright © 2019 Acta Automatica Sinica. All rights reserved.