Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine

PeerJ Comput Sci. 2021 Mar 30;7:e417. doi: 10.7717/peerj-cs.417. eCollection 2021.


In this paper, a method that uses a ground-penetrating radar (GPR) and the adaptive particle swarm support vector machine (SVM) method is proposed for detecting and recognizing hidden layer defects in highways. Three common road features, namely cracks, voids, and subsidence, were collected using ground-penetrating imaging. Image segmentation was performed on acquired images. Original features were extracted from thresholded binary images and were compressed using the kl algorithm. The SVM classification algorithm was used for condition classification. For parameter optimization of the SVM algorithm, the grid search method and particle swarm optimization algorithm were used. The recognition rate using the grid search method was 88.333%; the PSO approach often yielded local maxima, and the recognition rate was 86.667%; the improved adaptive PSO algorithm avoided local maxima and increased the recognition rate to 91.667%.

Keywords: 5 Ground penetrating radar (GPR); Feature extraction; Grid search method; Image segmentation; Particle swarm optimization (PSO); Support vector machine (SVM).

Grant support

This work was supported by the Key Scientific Research Projects of Higher Education Institutions in Henan Province (No.21A120006) and Henan Key Youth Teacher Research Project (2016GGJS-074). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.