An Improved Convolutional Neural Network System for Automatically Detecting Rebar in GPR Data

TL;DR

This paper introduces an improved CNN-based method using AlexNet for automatic rebar detection in GPR data, demonstrating higher accuracy especially in dense rebar arrangements, and analyzes the effects of window size and rebar distribution.

Contribution

It presents AlexNet as a superior alternative to traditional CNNs for rebar detection in GPR data, with detailed evaluation of rebar arrangements and window sizes.

Findings

AlexNet outperforms traditional CNNs, especially in dense rebar meshes.

Detection accuracy depends on window size, requiring sufficient information.

Sparse rebar arrangements are easier to detect than dense or uneven ones.

Abstract

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.