{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T09:36:19Z","timestamp":1772271379482,"version":"3.50.1"},"reference-count":25,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Projects of Higher Education Institutions in Henan Province","award":["21A120006"],"award-info":[{"award-number":["21A120006"]}]},{"name":"Henan Key Youth Teacher Research Project","award":["2016GGJS-074"],"award-info":[{"award-number":["2016GGJS-074"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>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%.<\/jats:p>","DOI":"10.7717\/peerj-cs.417","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T04:20:31Z","timestamp":1617078031000},"page":"e417","source":"Crossref","is-referenced-by-count":12,"title":["Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine"],"prefix":"10.7717","volume":"7","author":[{"given":"Xinyu","family":"Liu","sequence":"first","affiliation":[{"name":"CHANG\u2019AN University, Xian, China"},{"name":"School of Electric Power, North China University of Water Resource and Electric Power, Zhengzhou, China"},{"name":"Henan Wanli Road & Bridge Group Co. Ltd., Xuchang, China"}]},{"given":"Peiwen","family":"Hao","sequence":"additional","affiliation":[{"name":"CHANG\u2019AN University, Xian, China"}]},{"given":"Aihui","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhongyuan University of Technology, Zhengzhou, China"}]},{"given":"Liangqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Henan Wanli Road & Bridge Group Co. Ltd., Xuchang, China"}]},{"given":"Bo","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Electric Power, North China University of Water Resource and Electric Power, Zhengzhou, China"}]},{"given":"Xinyan","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Electric Power, North China University of Water Resource and Electric Power, Zhengzhou, China"}]}],"member":"4443","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"10.7717\/peerj-cs.417\/ref-1","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.sigpro.2016.05.016","article-title":"An overview of ground-penetrating radar signal processing techniques for road inspections","volume":"132","author":"Benedetto","year":"2017","journal-title":"Signal Processing"},{"key":"10.7717\/peerj-cs.417\/ref-2","doi-asserted-by":"crossref","first-page":"11687","DOI":"10.1109\/ACCESS.2017.2759509","article-title":"A high-order clustering algorithm based on dropout deep 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