An Improved Convolutional Neural Network System for Automatically Detecting Rebar in GPR Data
This work addresses the time-consuming and error-prone manual detection of rebar in concrete structures for civil engineering applications, but it is incremental as it applies an existing CNN method (AlexNet) to a specific domain.
The paper tackled the problem of automatically detecting rebar in Ground Penetration Radar (GPR) data, showing that AlexNet outperforms a traditional CNN, especially for densely distributed rebar meshes, with detection accuracy varying based on window size and rebar arrangement.
As a mature technology, Ground Penetration Radar (GPR) is now widely employed in detecting rebar and other embedded elements in concrete structures. Manually recognizing rebar from GPR data is a time-consuming and error-prone procedure. Although there are several approaches to automatically detect rebar, it is still challenging to find a high resolution and efficient method for different rebar arrangements, especially for closely spaced rebar meshes. As an improved Convolution Neural Network (CNN), AlexNet shows superiority over traditional methods in image recognition domain. Thus, this paper introduces AlexNet as an alternative solution for automatically detecting rebar within GPR data. In order to show the efficiency of the proposed approach, a traditional CNN is built as the comparative option. Moreover, this research evaluates the impacts of different rebar arrangements and different window sizes on the accuracy of results. The results revealed that: (1) AlexNet outperforms the traditional CNN approach, and its superiority is more notable when the rebar meshes are densely distributed; (2) the detection accuracy significantly varies with changing the size of splitting window, and a proper window should contain enough information about rebar; (3) uniformly and sparsely distributed rebar meshes are more recognizable than densely or unevenly distributed items, due to lower chances of signal interferences.