{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T07:16:16Z","timestamp":1774595776707,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T00:00:00Z","timestamp":1637539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003565","name":"Ministry of Land, Infrastructure and Transport","doi-asserted-by":"publisher","award":["21CTAP-C164242-01"],"award-info":[{"award-number":["21CTAP-C164242-01"]}],"id":[{"id":"10.13039\/501100003565","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Road surface detection is important for safely driving autonomous vehicles. This is because the knowledge of road surface conditions, in particular, dry, wet, and snowy surfaces, should be considered for driving control of autonomous vehicles. With the rise of deep learning technology, road surface detection methods using deep neural networks (DNN) have been widely used for developing road surface detection algorithms. To apply DNN in road surface detection, the dataset should be large and well-balanced for accurate and robust performance. However, most of the images of road surfaces obtained through usual data collection processes are not well-balanced. Most of the collected surface images tend to be of dry surfaces because road surface conditions are highly correlated with weather conditions. This could be a challenge in developing road surface detection algorithms. This paper proposes a method to balance the imbalanced dataset using CycleGAN to improve the performance of a road surface detection algorithm. CycleGAN was used to artificially generate images of wet and snow-covered roads. The road surface detection algorithm trained using the CycleGAN-augmented dataset had a better IoU than the method using imbalanced basic datasets. This result shows that CycleGAN-generated images can be used as datasets for road surface detection to improve the performance of DNN, and this method can help make the data acquisition process easy.<\/jats:p>","DOI":"10.3390\/s21227769","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7769","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Development of Road Surface Detection Algorithm Using CycleGAN-Augmented Dataset"],"prefix":"10.3390","volume":"21","author":[{"given":"Wansik","family":"Choi","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Pusan National University, Busan 46241, Korea"}]},{"given":"Jun","family":"Heo","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1455-9270","authenticated-orcid":false,"given":"Changsun","family":"Ahn","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Pusan National University, Busan 46241, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,22]]},"reference":[{"key":"ref_1","unstructured":"Wallman, C.-G., and \u00c5str\u00f6m, H. (2001). Friction Measurement Methods and the Correlation between Road Friction and Traffic Safety: A Literature Review, Statens v\u00e4g- och Transportforskningsinstitut."},{"key":"ref_2","unstructured":"Hippi, M., Juga, I., and Nurmi, P. (2010, January 5\u20137). A statistical forecast model for road surface friction. Proceedings of the In SIRWEC 15th International Road Weather Conference, Quebec City, QC, Canada."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1080\/10298436.2015.1039005","article-title":"Linking roadway crashes and tire\u2013pavement friction: A case study","volume":"18","author":"Najafi","year":"2017","journal-title":"Int. J. Pavement Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1002\/(SICI)1520-684X(199912)30:14<88::AID-SCJ9>3.0.CO;2-8","article-title":"Detection of road conditions with CCD cameras mounted on a vehicle","volume":"30","author":"Kuno","year":"1999","journal-title":"Syst. Comput. Jpn."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Holzmann, F., Bellino, M., Siegwart, R., and Bubb, H. (2006, January 4\u20136). Predictive estimation of the road-tire friction coefficient. Proceedings of the 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, Munich, Germany.","DOI":"10.1109\/CACSD-CCA-ISIC.2006.4776762"},{"key":"ref_6","unstructured":"Shinmoto, Y., Takagi, J., Egawa, K., Murata, Y., and Takeuchi, M. (1997, January 12). Road surface recognition sensor using an optical spatial filter. Proceedings of the Conference on Intelligent Transportation Systems, Boston, MA, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jokela, M., Kutila, M., and Le, L. (2009, January 27\u201329). Road condition monitoring system based on a stereo camera. Proceedings of the 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP.2009.5284724"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TCST.2011.2170838","article-title":"Robust estimation of road frictional coefficient","volume":"21","author":"Ahn","year":"2011","journal-title":"IEEE Trans. Control. Syst. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1115\/1.1870036","article-title":"Dynamic friction model-based tire-road friction estimation and emergency braking control","volume":"127","author":"Alvarez","year":"2005","journal-title":"J. Dyn. Syst. Meas. Control."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1080\/00423119608969210","article-title":"Road friction coefficient estimation for vehicle path prediction","volume":"25","author":"Liu","year":"1996","journal-title":"Veh. Syst. Dyn."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1076\/vesd.31.4.233.4231","article-title":"Estimation of tire-road friction using observer based identifiers","volume":"31","author":"Yi","year":"1999","journal-title":"Veh. Syst. Dyn."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Slavkovikj, V., Verstockt, S., De Neve, W., Van Hoecke, S., and Van de Walle, R. (2014, January 24\u201328). Image-based road type classification. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.409"},{"key":"ref_13","unstructured":"Seeger, C., M\u00fcller, A., Schwarz, L., and Manz, M. (2016, January 19). Towards road type classification with occupancy grids. Proceedings of the IVS Workshop, Gothenburg, Sweden."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102638","DOI":"10.1016\/j.jvcir.2019.102638","article-title":"Road surface condition classification using deep learning","volume":"64","author":"Cheng","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"\u0160abanovi\u010d, E., \u017duraulis, V., Prentkovskis, O., and Skrickij, V. (2020). Identification of road-surface type using deep neural networks for friction coefficient estimation. Sensors, 20.","DOI":"10.3390\/s20030612"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102680","DOI":"10.1109\/ACCESS.2020.2998427","article-title":"Lightweight semantic segmentation for road-surface damage recognition based on multiscale learning","volume":"8","author":"Shim","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s10514-020-09964-3","article-title":"Road surface detection and differentiation considering surface damages","volume":"45","author":"Rateke","year":"2021","journal-title":"Auton. Robot."},{"key":"ref_18","first-page":"88","article-title":"Road Surface State Recognition Based on Semantic Segmentation","volume":"15","author":"Wang","year":"2021","journal-title":"J. Highw. Transp. Res. Dev."},{"key":"ref_19","unstructured":"Liang, C., Ge, J., Zhang, W., Gui, K., Cheikh, F.A., and Ye, L. (2019, January 23\u201328). Winter road surface status recognition using deep semantic segmentation network. Proceedings of the International Workshop on Atmospheric Icing of Structures (IWAIS 2019), Reykjavik, Iceland."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lyu, Y., Bai, L., and Huang, X. (2019, January 26\u201329). Road segmentation using cnn and distributed lstm. Proceedings of the 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan.","DOI":"10.1109\/ISCAS.2019.8702174"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nolte, M., Kister, N., and Maurer, M. (2018, January 4\u20137). Assessment of deep convolutional neural networks for road surface classification. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569396"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0278364916679498","article-title":"1 year, 1000 km: The oxford robotcar dataset","volume":"36","author":"Maddern","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhao, A., Balakrishnan, G., Durand, F., Guttag, J.V., and Dalca, A.V. (2019, January 15\u201320). Data augmentation using learned transformations for one-shot medical image segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00874"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S., and Clark, C. (2020, January 20\u201321). Data augmentation for deep learning based cattle segmentation in precision livestock farming. Proceedings of the 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Hong Kong, China.","DOI":"10.1109\/CASE48305.2020.9216758"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Eigen, D., and Fergus, R. (2015, January 7\u201313). Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.304"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Neuhold, G., Ollmann, T., Rota Bulo, S., and Kontschieder, P. (2017, January 22\u201329). The mapillary vistas dataset for semantic understanding of street scenes. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.534"},{"key":"ref_30","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hoeser, T., and Kuenzer, C. (2020). Object detection and image segmentation with deep learning on earth observation data: A review-part i: Evolution and recent trends. Remote Sens., 12.","DOI":"10.3390\/rs12101667"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7769\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:34:06Z","timestamp":1760168046000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,22]]},"references-count":33,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21227769"],"URL":"https:\/\/doi.org\/10.3390\/s21227769","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,22]]}}}