Zhou Shuangxi, Pan Yuan, Huang Xiaosheng, Yang Dan, Ding Yang, Duan Runtao
School of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang 330013, China.
School of Software, East China Jiao Tong University, Nanchang 330013, China.
Materials (Basel). 2022 Jun 1;15(11):3940. doi: 10.3390/ma15113940.
Structural cracks in concrete have a significant influence on structural safety, so it is necessary to detect and monitor concrete cracks. Deep learning is a powerful tool for detecting cracks in concrete structures. However, it requires a large quantity of training samples and is costly in terms of computational time. In order to solve these difficulties, a deep learning target detection framework combining texture features with concrete crack data is proposed. Texture features and pre-processed concrete data are merged to increase the number of feature channels in order to reduce the demand of training samples for the model and improve training speed. With this framework, concrete crack detection can be realized even with a limited number of samples. To accomplish this aim, self-made steel fiber reinforced concrete crack data is used for comparison between our framework and those without texture feature mergence or pre-processed concrete data. The experimental results show that the number of parameters that need to be fitted in the model training and training time can be correspondingly reduced and the detection accuracy can also be improved.
混凝土结构裂缝对结构安全有重大影响，因此有必要对混凝土裂缝进行检测与监测。深度学习是检测混凝土结构裂缝的有力工具。然而，它需要大量的训练样本，且计算时间成本高昂。为了解决这些难题，提出了一种将纹理特征与混凝土裂缝数据相结合的深度学习目标检测框架。将纹理特征与预处理后的混凝土数据合并，以增加特征通道数量，从而减少模型对训练样本的需求并提高训练速度。借助该框架，即使样本数量有限也能实现混凝土裂缝检测。为实现这一目标，使用自制的钢纤维增强混凝土裂缝数据，将我们的框架与未进行纹理特征合并或未对混凝土数据进行预处理的框架进行比较。实验结果表明，模型训练中需要拟合的参数数量和训练时间可相应减少，检测精度也能得到提高。
