{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T15:40:45Z","timestamp":1787240445627,"version":"build-2736575974"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T00:00:00Z","timestamp":1649894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Guangdong Provincial Department of Agriculture\u2019s Modern Agricultural Innovation Team Program for Animal Husbandry Robotics","award":["Grant No. 2019KJ129"],"award-info":[{"award-number":["Grant No. 2019KJ129"]}]},{"name":"the State Key Research Program of China","award":["Grant No. 2016YFD0700101"],"award-info":[{"award-number":["Grant No. 2016YFD0700101"]}]},{"name":"the Vehicle Soil Parameter Collection and Testing Project","award":["Grant No. 4500-F21445"],"award-info":[{"award-number":["Grant No. 4500-F21445"]}]},{"name":"Special project of Guangdong Provincial Rural Revitalization Strategy in 2020 (YCN [2020] No. 39)","award":["Fund No. 200-2018-XMZC-0001-107-0130"],"award-info":[{"award-number":["Fund No. 200-2018-XMZC-0001-107-0130"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ground penetrating radar (GPR) detection is a popular technology in civil engineering. Because of its advantages of non-destructive testing (NDT) and high work efficiency, GPR is widely used to detect hard foreign objects in soil. However, the interpretation of GPR images relies heavily on the work experience of researchers, which may lead to problems of low detection efficiency and a high false recognition rate. Therefore, this paper proposes a real-time detection technology of GPR based on deep learning for the application of soil foreign object detection. In this study, the GPR image signal is obtained in real time by the GPR instrument and software, and the image signals are preprocessed to improve the signal-to-noise ratio of the GPR image signals and improve the image quality. Then, in view of the problem that YOLOv5 poorly detects small targets, this study improves the problems of false detection and missed detection in real-time GPR detection by improving the network structure of YOLOv5, adding an attention mechanism, data enhancement, and other means. Finally, by establishing a regression equation for the position information of the ground penetrating radar, the precise localization of the foreign matter in the underground soil is realized.<\/jats:p>","DOI":"10.3390\/rs14081895","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T02:39:31Z","timestamp":1650335971000},"page":"1895","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Application of an Improved YOLOv5 Algorithm in Real-Time Detection of Foreign Objects by Ground Penetrating Radar"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6757-3255","authenticated-orcid":false,"given":"Zhi","family":"Qiu","sequence":"first","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"Ministry of Education Key Technologies and Equipment Laboratory of Agricultural Machinery and Equipment in South China, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuoxi","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"Ministry of Education Key Technologies and Equipment Laboratory of Agricultural Machinery and Equipment in South China, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoji","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"Ministry of Education Key Technologies and Equipment Laboratory of Agricultural Machinery and Equipment in South China, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyuan","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"Ministry of Education Key Technologies and Equipment Laboratory of Agricultural Machinery and Equipment in South China, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"Ministry of Education Key Technologies and Equipment Laboratory of Agricultural Machinery and Equipment in South China, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Borui","family":"Xiang","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"Ministry of Education Key Technologies and Equipment Laboratory of Agricultural Machinery and Equipment in South China, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, T., Xu, X., Wang, C., Li, Z., and Li, D. (2021). From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production. Agriculture, 11.","DOI":"10.3390\/agriculture11020145"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1016\/j.conbuildmat.2017.12.157","article-title":"A nondestructive evaluation method for semi-rigid base cracking condition of asphalt pavement","volume":"162","author":"Zang","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104353","DOI":"10.1016\/j.still.2019.104353","article-title":"An effective FDTD model for GPR to detect the material of hard objects buried in tillage soil layer","volume":"195","author":"Li","year":"2019","journal-title":"Soil Tillage Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.jappgeo.2013.01.011","article-title":"Detection of subsurface metallic utilities by means of a SAP technique: Comparing MUSIC- and SVM-based approaches","volume":"97","author":"Meschino","year":"2013","journal-title":"J. Appl. Geophys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1080\/10298436.2018.1559317","article-title":"Deep learning-based underground object detection for urban road pavement","volume":"21","author":"Kim","year":"2020","journal-title":"Int. J. Pavement Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.jappgeo.2018.03.005","article-title":"Analyses of GPR signals for characterization of ground conditions in urban areas","volume":"152","author":"Hong","year":"2018","journal-title":"J. Appl. Geophys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.jappgeo.2013.03.010","article-title":"GPR abilities in investigation of the pavement transversal cracks","volume":"97","author":"Sudyka","year":"2013","journal-title":"J. Appl. Geophys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.autcon.2017.03.004","article-title":"Corrosiveness mapping of bridge decks using image-based analysis of GPR data","volume":"80","author":"Abouhamad","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.autcon.2011.09.010","article-title":"Structural analysis of the Roman Bibei bridge (Spain) based on GPR data and numerical modelling","volume":"22","author":"Solla","year":"2012","journal-title":"Autom. Constr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.autcon.2018.03.002","article-title":"An anomalous event detection and tracking method for a tunnel look-ahead ground prediction system","volume":"91","author":"Wei","year":"2018","journal-title":"Automat. Constr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.conbuildmat.2018.07.039","article-title":"Specific evaluation of tunnel lining multi-defects by all-refined GPR simulation method using hybrid algorithm of FETD and FDTD","volume":"185","author":"Feng","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cuenca-Garc\u00eda, C., Risb\u00f8l, O., Bates, C.R., Stamnes, A.A., Skoglund, F., \u00d8deg\u00e5rd, \u00d8., Viberg, A., Koivisto, S., Fuglsang, M., and Gabler, M. (2020). Sensing Archaeology in the North: The Use of Non-Destructive Geophysical and Remote Sensing Methods in Archaeology in Scandinavian and North Atlantic Territories. Remote Sens., 12.","DOI":"10.3390\/rs12183102"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1071\/EG08107","article-title":"Urban archaeological investigations using surface 3D Ground Penetrating Radar and Electrical Resistivity Tomography methods","volume":"40","author":"Papadopoulos","year":"2018","journal-title":"Explor. Geophys."},{"key":"ref_14","first-page":"264","article-title":"On a reliable assessment of the location and size of rebar in concrete structures from radargrams of ground-penetrating radar","volume":"58","author":"Ramya","year":"2016","journal-title":"Insight Non-Destr. Test. Cond. Monit."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, W., Cui, X., Guo, L., Chen, J., Chen, X., and Cao, X. (2016). Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform. Remote Sens. Basel, 8.","DOI":"10.3390\/rs8050430"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jin, Y., and Duan, Y. (2020). Wavelet Scattering Network-Based Machine Learning for Ground Penetrating Radar Imaging: Application in Pipeline Identification. Remote Sens., 12.","DOI":"10.3390\/rs12213655"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107839","DOI":"10.1016\/j.measurement.2020.107839","article-title":"Identifying concrete structure defects in GPR image","volume":"160","author":"Jiao","year":"2020","journal-title":"Measurement"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103830","DOI":"10.1016\/j.autcon.2021.103830","article-title":"Automatic recognition of tunnel lining elements from GPR images using deep convolutional networks with data augmentation","volume":"130","author":"Qin","year":"2021","journal-title":"Automat. Constr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.autcon.2018.02.017","article-title":"An algorithm for automatic localization and detection of rebars from GPR data of concrete bridge decks","volume":"89","author":"Dinh","year":"2018","journal-title":"Automat. Constr."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhao, Z., Luo, Y., and Qiu, Z. (2020). Real-Time Pattern-Recognition of GPR Images with YOLO v3 Implemented by Tensorflow. Sensors, 20.","DOI":"10.3390\/s20226476"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"121949","DOI":"10.1016\/j.conbuildmat.2020.121949","article-title":"Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm","volume":"273","author":"Li","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1016\/j.conbuildmat.2018.08.190","article-title":"Location of reinforcement and moisture assessment in reinforced concrete with a double receiver GPR antenna","volume":"188","author":"Agred","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.cageo.2013.04.012","article-title":"Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar","volume":"58","author":"Maas","year":"2013","journal-title":"Comput. Geosci. UK"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Feng, D., Wang, X., Wang, X., Ding, S., and Zhang, H. (2021). Deep Convolutional Denoising Autoencoders with Network Structure Optimization for the High-Fidelity Attenuation of Random GPR Noise. Remote Sens., 13.","DOI":"10.3390\/rs13091761"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107817","DOI":"10.1016\/j.patcog.2021.107817","article-title":"Deep residual pooling network for texture recognition","volume":"112","author":"Mao","year":"2021","journal-title":"Pattern Recogn."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., Hsieh, J.W., and Yeh, I.H. (2020, January 14\u201319). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/s10115-020-01538-0","article-title":"Saliency-based YOLO for single target detection","volume":"63","author":"Hu","year":"2021","journal-title":"Knowl. Inf. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and PATTERN recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_30","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yan, B., Fan, P., Lei, X., Liu, Z., and Yang, F. (2021). A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5. Remote Sens., 13.","DOI":"10.3390\/rs13091619"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_35","first-page":"51","article-title":"Dynamic categorization of 3D objects for mobile service robots","volume":"48","year":"2020","journal-title":"Ind. Robot. Int. J. Robot. Res. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Cui, Y., Srinivas, A., Qian, R., Lin, T.Y., Cubuk, E.D., Le, Q.V., and Zoph, B. (2021, January 20\u201325). Simple copy-paste is a strong data augmentation method for instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00294"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yu, J., Jiang, Y., Wang, Z., Cao, Z., and Huang, T. (2016, January 15\u201319). UnitBox: An Advanced Object Detection Network. Proceedings of the 24th ACM international conference on Multimedia, New York, NY, USA.","DOI":"10.1145\/2964284.2967274"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, H., An, L., Chu, V.W., Stow, D.A., Liu, X., and Ding, Q. (2021). Learning Adjustable Reduced Downsampling Network for Small Object Detection in Urban Environments. Remote Sens., 13.","DOI":"10.3390\/rs13183608"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Ren, D., Liu, W., Ye, R., Hu, Q., and Zuo, W. (2021). Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation. IEEE Trans. Cybern.","DOI":"10.1109\/TCYB.2021.3095305"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1895\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:54:14Z","timestamp":1760136854000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1895"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,14]]},"references-count":41,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14081895"],"URL":"https:\/\/doi.org\/10.3390\/rs14081895","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,14]]}}}