基于深度学习的桥梁裂缝检测算法研究

Research on Detection Algorithm for Bridge Cracks Based on Deep Learning

传统的图像处理算法不能很好地对桥梁裂缝进行检测,而经典的深度学习模型直接用于桥梁裂缝的检测,效果不甚理想.针对这些问题,本文提出了一种基于深度学习的桥梁裂缝检测算法.首先,利用滑动窗口算法将桥梁裂缝图像切分为较小的桥梁裂缝面元图像和桥梁背景面元图像,并根据对面元图像的分析,提出一种基于卷积神经网络(Convolutional neural networks, CNN)的DBCC (Deep bridge crack classify)分类模型,用于桥梁背景面元和桥梁裂缝面元的识别.然后,基于DBCC分类模型结合改进的窗口滑动算法对桥梁裂缝进行检测.最后,采用图像金字塔和感兴趣区域(Region of interest,ROI)结合的搜索策略对算法进行加速.实验结果表明:与传统算法相比,本文算法具有更好的识别效果和更强的泛化能力.

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.