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

• DOI: 10.7717/peerj-cs.417
• Publication Date: 2021-03-30T08:20:31Z
• Authors: Xinyu Liu; Peiwen Hao; Aihui Wang; Liangqi Zhang; Bo Gu; Xinyan Lu
• Keywords: Image segmentation; Electronic computers. Computer science; Particle swarm optimization (PSO); Adaptive and Self-Organizing Systems; 5 Ground penetrating radar (GPR); 0202 electrical engineering, electronic engineering, information engineering; Feature extraction; Support vector machine (SVM); QA75.5-76.95; 02 engineering and technology; Grid search method

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%.