Affiliation:
1. College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Abstract
Bridge crack detection based on deep learning is a research area of great interest and difficulty in the field of bridge health detection. This study aimed to investigate the effectiveness of coupling a deep learning framework (DLF) with a convolutional neural network (CNN) for bridge crack detection. A dataset consisting of 2068 bridge crack images was randomly split into training, verification, and testing sets with a ratio of 8:1:1, respectively. Several CNN models, including Faster R-CNN, Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO)-v5(x), U-Net, and Pyramid Scene Parsing Network (PSPNet), were used to conduct experiments using the PyTorch, TensorFlow2, and Keras frameworks. The experimental results show that the Harmonic Mean (F1) values of the detection results of the Faster R-CNN and SSD models under the Keras framework are relatively large (0.76 and 0.67, respectively, in the object detection model). The YOLO-v5(x) model of the TensorFlow2 framework achieved the highest F1 value of 0.67. In semantic segmentation models, the U-Net model achieved the highest detection result accuracy (AC) value of 98.37% under the PyTorch framework. The PSPNet model achieved the highest AC value of 97.86% under the TensorFlow2 framework. These experimental results provide optimal coupling efficiency parameters of a DLF and CNN for bridge crack detection. A more accurate and efficient DLF and CNN model for bridge crack detection has been obtained, which has significant practical application value.
Funder
National Natural Science Foundation of China
the Key Scientific Research Projects of Colleges and Universities in Henan Province
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference51 articles.
1. A concrete crack recognition method based on progressive cascade convolution neural network;Lu;Ind. Constr.,2021
2. Zhou, S., Pan, Y., Huang, X., Yang, D., Ding, Y., and Duan, R. (2022). Crack texture feature identification of fiber reinforced concrete based on deep learning. Materials, 15.
3. Ren, J., Zhao, G., Ma, Y., Zhao, D., Liu, T., and Yan, J. (2022). Automatic pavement crack detection fusing attention mechanism. Electronics, 11.
4. Detection of sealed and unsealed cracks with complex backgrounds using deep convolutional neural network;Huyan;Autom. Constr.,2019
5. Bridge crack classification and measurement method based on deep convolutional neural network;Liang;Comput. Appl.,2020
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献