Liu Xinyu, Hao Peiwen, Wang Aihui, Zhang Liangqi, Gu Bo, Lu Xinyan
CHANG'AN University, Xian, China.
School of Electric Power, North China University of Water Resource and Electric Power, Zhengzhou, China.
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%.
本文提出了一种利用探地雷达（GPR）和自适应粒子群支持向量机（SVM）方法来检测和识别高速公路隐藏层缺陷的方法。利用探地成像收集了三种常见的道路特征，即裂缝、空洞和沉降。对采集到的图像进行图像分割。从阈值化的二值图像中提取原始特征，并使用kl算法进行压缩。使用支持向量机分类算法进行状态分类。对于支持向量机算法的参数优化，采用了网格搜索法和粒子群优化算法。使用网格搜索法的识别率为88.333%；粒子群优化方法常常产生局部最大值，识别率为86.667%；改进的自适应粒子群算法避免了局部最大值，将识别率提高到91.667%。
