{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T16:16:28Z","timestamp":1792253788468,"version":"4.4.0"},"reference-count":52,"publisher":"Elsevier BV","issue":"31","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"vor","delay-in-days":105,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52578540"],"award-info":[{"award-number":["52578540"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52478477"],"award-info":[{"award-number":["52478477"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer-Aided Civil and Infrastructure Engineering"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1111\/mice.70157","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T11:57:39Z","timestamp":1763985459000},"page":"6475-6492","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"title":["Three\u2010dimensional reconstruction of loose defects in semi\u2010rigid base layers using enhanced deep learning and point cloud from GPR images"],"prefix":"10.1016","volume":"40","author":[{"given":"Bei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhui","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjian","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quansheng","family":"Zang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"12","key":"10.1111\/mice.70157_bb0010","doi-asserted-by":"crossref","first-page":"8675","DOI":"10.1007\/s00521-019-04359-7","article-title":"A dynamic ensemble learning algorithm for neural networks","volume":"32","author":"Alam","year":"2020","journal-title":"Neural Computing and Applications"},{"key":"10.1111\/mice.70157_bb0015","doi-asserted-by":"crossref","unstructured":"Bai, M., Qi, Y., Li, Z., Wang, Q., & Tian, G. (2023). Research on intelligent detection of urban road subgrade disease based on deep learning algorithm. In H., Zaidi, Y.S., Shmaliy, H., Meng, H., Kolivand, Y., Sun, J., Luo, & M., Alazab (Eds.), Fifth international conference on artificial intelligence and computer science (AICS 2023) (Vol. 12803, pp. 950\u2013957), SPIE \u2013 The International Society for Optics and Photonics.","DOI":"10.1117\/12.3009448"},{"key":"10.1111\/mice.70157_bb0020","doi-asserted-by":"crossref","first-page":"139082","DOI":"10.1109\/ACCESS.2023.3340895","article-title":"Automated construction site monitoring based on improved yolov8\u2010seg instance segmentation algorithm","volume":"11","author":"Bai","year":"2023","journal-title":"IEEE Access"},{"issue":"11","key":"10.1111\/mice.70157_bb0025","doi-asserted-by":"crossref","first-page":"2896","DOI":"10.3390\/rs15112896","article-title":"Gas plume target detection in multibeam water column image using deep residual aggregation structure and attention mechanism","volume":"15","author":"Chen","year":"2023","journal-title":"Remote Sensing"},{"issue":"17","key":"10.1111\/mice.70157_bb0030","doi-asserted-by":"crossref","first-page":"2472","DOI":"10.1111\/mice.13070","article-title":"Iterative application of generative adversarial networks for improved buried pipe detection from images obtained by ground\u2010penetrating radar","volume":"38","author":"Chun","year":"2023","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"issue":"3","key":"10.1111\/mice.70157_bb0035","doi-asserted-by":"crossref","first-page":"830","DOI":"10.3390\/su12030830","article-title":"A sensitivity and robustness analysis of GPR and ANN for high\u2010performance concrete compressive strength prediction using a Monte Carlo simulation","volume":"12","author":"Dao","year":"2020","journal-title":"Sustainability"},{"issue":"1","key":"10.1111\/mice.70157_bb0040","doi-asserted-by":"crossref","DOI":"10.1061\/(ASCE)CP.1943-5487.0001062","article-title":"Robotic inspection of underground utilities for construction survey using a ground penetrating radar","volume":"37","author":"Feng","year":"2023","journal-title":"Journal of Computing in Civil Engineering"},{"issue":"5","key":"10.1111\/mice.70157_bb0045","doi-asserted-by":"crossref","first-page":"4527","DOI":"10.1109\/JSEN.2022.3164707","article-title":"Improving 3D metric GPR imaging using automated data collection and learning\u2010based processing","volume":"23","author":"Feng","year":"2022","journal-title":"IEEE Sensors Journal"},{"issue":"10","key":"10.1111\/mice.70157_bb0050","doi-asserted-by":"crossref","DOI":"10.1002\/stc.3050","article-title":"Non\u2010destructive evaluation of longitudinal cracking in semi\u2010rigid asphalt pavements using FWD deflection data","volume":"29","author":"Fu","year":"2022","journal-title":"Structural Control and Health Monitoring"},{"key":"10.1111\/mice.70157_bb0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.108077","article-title":"Autonomous pavement distress detection using ground penetrating radar and region\u2010based deep learning","volume":"164","author":"Gao","year":"2020","journal-title":"Measurement"},{"key":"10.1111\/mice.70157_bb0060","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., & Girshick, R. (2017). Mask R\u2010CNN. In IEEE international conference on computer vision (ICCV) (pp. 2980\u20132988).","DOI":"10.1109\/ICCV.2017.322"},{"issue":"9","key":"10.1111\/mice.70157_bb0065","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1111\/mice.13374","article-title":"Capturing uncertainty intuition in road maintenance decision\u2010making using an evidential neural network","volume":"40","author":"Hei","year":"2025","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"issue":"12","key":"10.1111\/mice.70157_bb0070","doi-asserted-by":"crossref","first-page":"6201","DOI":"10.1109\/TAP.2013.2283604","article-title":"Resistive loaded antenna for ground penetrating radar inside a bottom hole assembly","volume":"61","author":"H\u00f6finghoff","year":"2013","journal-title":"IEEE Transactions on Antennas and Propagation"},{"key":"10.1111\/mice.70157_bb0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2025.106140","article-title":"Multi\u2010scale GAN\u2010driven GPR data inversion for monitoring urban road substructure","volume":"174","author":"Hou","year":"2025","journal-title":"Automation in Construction"},{"issue":"8","key":"10.1111\/mice.70157_bb0080","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2024.3420883","article-title":"Smoke and fire detection based on yolov7 with convolutional structure reparameterization and lightweighting","volume":"8","author":"Hu","year":"2024","journal-title":"IEEE Sensors Letters"},{"key":"10.1111\/mice.70157_bb0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.dibe.2024.100591","article-title":"Three\u2010dimensional ground\u2010penetrating radar\u2010based feature point tensor voting for semi\u2010rigid base asphalt pavement crack detection","volume":"21","author":"Huang","year":"2025","journal-title":"Developments in the Built Environment"},{"issue":"1","key":"10.1111\/mice.70157_bb0090","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1038\/s40494-025-01620-2","article-title":"Using the improved yolov7\u2010seg model to segment symbols from rock art images","volume":"13","author":"Huo","year":"2025","journal-title":"npj Heritage Science"},{"issue":"8","key":"10.1111\/mice.70157_bb0095","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.12845","article-title":"Integrating structural control, health monitoring, and energy harvesting for smart cities","volume":"38","author":"Javadinasab Hormozabad","year":"2021","journal-title":"Expert Systems"},{"issue":"10","key":"10.1111\/mice.70157_bb0100","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.3390\/agronomy14102403","article-title":"Identification and location method of bitter gourd picking point based on improved yolov5\u2010seg","volume":"14","author":"Jiang","year":"2024","journal-title":"Agronomy"},{"issue":"6","key":"10.1111\/mice.70157_bb0105","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1111\/mice.12912","article-title":"Vorogrid: A static\u2010aware variable\u2010density voronoi mesh to design the tube structure tessellation of tall buildings","volume":"38","author":"Laccone","year":"2023","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"issue":"9","key":"10.1111\/mice.70157_bb0110","first-page":"1105","article-title":"A grid\u2010based classification and box\u2010based detection fusion model based on yolov5","volume":"38","author":"Li","year":"2023","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"key":"10.1111\/mice.70157_bb0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2025.141179","article-title":"GPR image denoising based on unpaired data: Enhancing defect detection inside tunnels by tid\u2010cyclegan","volume":"475","author":"Li","year":"2025","journal-title":"Construction and Building Materials"},{"issue":"22","key":"10.1111\/mice.70157_bb0120","doi-asserted-by":"crossref","first-page":"6476","DOI":"10.3390\/s20226476","article-title":"Real\u2010time pattern\u2010recognition of gpr images with yolo v3 implemented by tensorflow","volume":"20","author":"Li","year":"2020","journal-title":"Sensors"},{"key":"10.1111\/mice.70157_bb0125","doi-asserted-by":"crossref","unstructured":"Lin, T.\u2010Y., Goyal, P., Girshick, R., He, K., & Doll\u00e1r, P., (2017). Focal loss for dense object detection. In R., Cucchiara, Y., Matsushita, N., Sebe, & S., Soatto (Eds.) Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2980\u20132988), IEEE.","DOI":"10.1109\/ICCV.2017.324"},{"issue":"1","key":"10.1111\/mice.70157_bb0130","article-title":"Automatic detection of sports injuries based on multimedia intelligent 3D images","volume":"2023","author":"Liu","year":"2023","journal-title":"Advances in Multimedia"},{"issue":"2","key":"10.1111\/mice.70157_bb0135","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","article-title":"Deep learning for generic object detection: A survey","volume":"128","author":"Liu","year":"2020","journal-title":"International Journal of Computer Vision"},{"issue":"5","key":"10.1111\/mice.70157_bb0140","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1111\/mice.12501","article-title":"Image\u2010based crack assessment of bridge piers using unmanned aerial vehicles and three\u2010dimensional scene reconstruction","volume":"35","author":"Liu","year":"2020","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"issue":"10","key":"10.1111\/mice.70157_bb0145","doi-asserted-by":"crossref","first-page":"13511","DOI":"10.1109\/TITS.2024.3403144","article-title":"Intelligent assessment of pavement structural conditions: A novel Femvit classification network for GPR images","volume":"25","author":"Liu","year":"2024","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"6","key":"10.1111\/mice.70157_bb0150","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.3390\/rs13061081","article-title":"Application of combining yolo models and 3d GPR images in road detection and maintenance","volume":"13","author":"Liu","year":"2021","journal-title":"Remote Sensing"},{"key":"10.1111\/mice.70157_bb0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2022.104689","article-title":"Automatic pixel\u2010level detection of vertical cracks in asphalt pavement based on GPR investigation and improved mask R\u2010CNN","volume":"146","author":"Liu","year":"2023","journal-title":"Automation in Construction"},{"key":"10.1111\/mice.70157_bb0160","article-title":"Automatic pixel\u2010level detection of vertical cracks in asphalt pavement based on GPR investigation and improved mask R\u2010CNN","volume":"144","author":"Liu","year":"2023","journal-title":"Automation in Construction"},{"key":"10.1111\/mice.70157_bb0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105506","article-title":"Lightweight deep learning model for identifying tunnel lining defects based on GPR data","volume":"165","author":"Luo","year":"2024","journal-title":"Automation in Construction"},{"key":"10.1111\/mice.70157_bb0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.jappgeo.2024.105397","article-title":"The ground penetrating radar response recognition of hidden dam defects using multi\u2010output convolutional neural network","volume":"225","author":"Ma","year":"2024","journal-title":"Journal of Applied Geophysics"},{"issue":"7","key":"10.1111\/mice.70157_bb0175","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"Minaee","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"2","key":"10.1111\/mice.70157_bb0180","first-page":"143","article-title":"New standardization method of MR image intensity for robust segmentation","volume":"19","author":"Ny\u00fal","year":"2000","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"10","key":"10.1111\/mice.70157_bb0185","doi-asserted-by":"crossref","first-page":"6393","DOI":"10.1007\/s00521-019-04146-4","article-title":"FEMA: A finite element machine for fast learning","volume":"32","author":"Pereira","year":"2020","journal-title":"Neural Computing and Applications"},{"key":"10.1111\/mice.70157_bb0190","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S., (2019). Generalized intersection over union: A metric and a loss for bounding box regression. In A., Gupta, D., Hoiem, G., Hua, & Z., Tu (Eds.), Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 658\u2013666), IEEE.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"10.1111\/mice.70157_bb0195","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U\u2010net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer\u2010Assisted Intervention \u2013 MICCAI 2015 (Lecture Notes in Computer Science, vol. 9351) (pp. 234\u2013241). Springer, Cham.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10.1111\/mice.70157_bb0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.compstruc.2022.106918","article-title":"Convolutional networks and transformers for intelligent road tunnel investigations","volume":"275","author":"Rosso","year":"2023","journal-title":"Computers & Structures"},{"issue":"17","key":"10.1111\/mice.70157_bb0205","doi-asserted-by":"crossref","first-page":"4190","DOI":"10.3390\/rs14174190","article-title":"Fast segmentation and dynamic monitoring of time\u2010lapse 3D GPR data based on U\u2010NET","volume":"14","author":"Shang","year":"2022","journal-title":"Remote Sensing"},{"key":"10.1111\/mice.70157_bb0210","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.patrec.2024.05.001","article-title":"Mod\u2010yolo: Multispectral object detection based on transformer dual\u2010stream yolo","volume":"183","author":"Shao","year":"2024","journal-title":"Pattern Recognition Letters"},{"key":"10.1111\/mice.70157_bb0215","doi-asserted-by":"crossref","unstructured":"Shepard, D. (1968). A two\u2010dimensional interpolation function for irregularly\u2010spaced data. In Proceedings of the 1968 ACM national conference (pp. 517\u2013524), New York, NY, USA. ACM.","DOI":"10.1145\/800186.810616"},{"issue":"4","key":"10.1111\/mice.70157_bb0220","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo\u2010nas","volume":"5","author":"Terven","year":"2023","journal-title":"Machine Learning and Knowledge Extraction"},{"key":"10.1111\/mice.70157_bb0225","unstructured":"Tong, Z., Chen, Y., Xu, Z., & Yu, R. (2023). Wise\u2010IoU: bounding box regression loss with dynamic focusing mechanism. arXiv preprint arXiv:2301.10051."},{"issue":"1","key":"10.1111\/mice.70157_bb0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2024.111558","article-title":"LEHP\u2010DETR: a model with backbone improved and hybrid encoding innovated for flax capsule detection","volume":"28","author":"Wang","year":"2025","journal-title":"iScience"},{"issue":"1","key":"10.1111\/mice.70157_bb0235","doi-asserted-by":"crossref","first-page":"162","DOI":"10.3390\/s25010162","article-title":"Three\u2010dimensional reconstruction of road structural defects using GPR investigation and back\u2010projection algorithm","volume":"25","author":"Wang","year":"2024","journal-title":"Sensors"},{"key":"10.1111\/mice.70157_bb0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.jappgeo.2024.105491","article-title":"Road underground defect detection in ground penetrating radar images based on an improved yolov5s model","volume":"229","author":"Xue","year":"2024","journal-title":"Journal of Applied Geophysics"},{"issue":"12","key":"10.1111\/mice.70157_bb0245","first-page":"1633","article-title":"Two\u2010step rapid inspection of underwater concrete bridge piers via an improved yolov7\u2010based detector","volume":"39","author":"Yu","year":"2024","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"key":"10.1111\/mice.70157_bb0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2024.139081","article-title":"Identification and 3d reconstruction of semi\u2010rigid base loose distress from GPR B\u2010scan using generative adversarial network","volume":"455","author":"Zhang","year":"2024","journal-title":"Construction and Building Materials"},{"key":"10.1111\/mice.70157_bb0255","first-page":"1","article-title":"Excavator 3D pose estimation from point cloud with self\u2010supervised deep learning","author":"Zhang","year":"2025","journal-title":"Computer\u2010Aided Civil and Infrastructure Engineering"},{"issue":"1","key":"10.1111\/mice.70157_bb0260","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s40328-023-00403-0","article-title":"Ground penetrating radar forward modeling of roads based on random media model","volume":"58","author":"Zhang","year":"2023","journal-title":"Acta Geodaetica et Geophysica"},{"key":"10.1111\/mice.70157_bb0265","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., & Ren, D. (2020). Distance\u2010IoU loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, pp. 12993\u201313000), AAAI Press.","DOI":"10.1609\/aaai.v34i07.6999"}],"container-title":["Computer-Aided Civil and Infrastructure Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1093968726017810?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1093968726017810?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/mice.70157","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T09:53:47Z","timestamp":1773654827000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1093968726017810"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":52,"journal-issue":{"issue":"31","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["S1093968726017810"],"URL":"https:\/\/doi.org\/10.1111\/mice.70157","relation":{},"ISSN":["1093-9687"],"issn-type":[{"value":"1093-9687","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Three\u2010dimensional reconstruction of loose defects in semi\u2010rigid base layers using enhanced deep learning and point cloud from GPR images","name":"articletitle","label":"Article Title"},{"value":"Computer-Aided Civil and Infrastructure Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1111\/mice.70157","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Copyright \u00a9 2025 The Author(s). Computer\u2010Aided Civil and Infrastructure Engineering published by Wiley Periodicals LLC on behalf of Editor. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}