  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.
