The catastrophic accidents of large concrete structures are mostly caused by small cracks,so it is very important to detect cracks during the service life of concrete structures.At present,the non-destructive detection algorithm for concrete cracks based on deep learning is developing rapidly,but most of them do not take the characteristics of crack information into account,so the detection accuracy still has room for further improvement.In this paper,an improved ResNet method for non-destructive de-tection of concrete cracks was proposed.Namely the residual neural network ResNet was used as the basic model of crack detec-tion,and the attention mechanism module was inserted to improve the model representation ability,so that it can effectively cap-ture the important feature information in the crack image,and improve the accuracy and robustness of the detection.At the same time,the transfer learning strategy was used to transfer the training results of the ResNet model on the complex data set to the crack data set,saving training time and computing resources.The results showed that the accuracy of the improved ResNet algo-rithm for crack detection was as high as 98.80%,which was 3.24%higher than that of the original ResNet algorithm.The rele-vant experience can be a reference in the construction of similar improved algorithms.
关键词
混凝土裂缝/裂缝检测/残差神经网络/注意力机制/迁移学习
Key words
crack of concrete/crack detection/residual neural network/attention mechanism/transfer learning
维普
万方数据