{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T10:42:19Z","timestamp":1777459339899,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2015,11,24]],"date-time":"2015-11-24T00:00:00Z","timestamp":1448323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61473303"],"award-info":[{"award-number":["61473303"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Road detection is an essential component of field robot navigation systems. Vision sensors play an important role in road detection for their great potential in environmental perception. In this paper, we propose a hierarchical vision sensor-based method for robust road detection in challenging road scenes. More specifically, for a given road image captured by an on-board vision sensor, we introduce a multiple population genetic algorithm (MPGA)-based approach for efficient road vanishing point detection. Superpixel-level seeds are then selected in an unsupervised way using a clustering strategy. Then, according to the GrowCut framework, the seeds proliferate and iteratively try to occupy their neighbors. After convergence, the initial road segment is obtained. Finally, in order to achieve a globally-consistent road segment, the initial road segment is refined using the conditional random field (CRF) framework, which integrates high-level information into road detection. We perform several experiments to evaluate the common performance, scale sensitivity and noise sensitivity of the proposed method. The experimental results demonstrate that the proposed method exhibits high robustness compared to the state of the art.<\/jats:p>","DOI":"10.3390\/s151129594","type":"journal-article","created":{"date-parts":[[2015,11,24]],"date-time":"2015-11-24T12:08:12Z","timestamp":1448366892000},"page":"29594-29617","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Vision Sensor-Based Road Detection for Field Robot Navigation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7968-0106","authenticated-orcid":false,"given":"Keyu","family":"Lu","sequence":"first","affiliation":[{"name":"College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangjing","family":"An","sequence":"additional","affiliation":[{"name":"College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hangen","family":"He","sequence":"additional","affiliation":[{"name":"College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"860","DOI":"10.3390\/s100100860","article-title":"Vision-Based Traffic Data Collection Sensor for Automotive Applications","volume":"10","author":"Llorca","year":"2010","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9046","DOI":"10.3390\/s140509046","article-title":"Robust Curb Detection with Fusion of 3D-Lidar and Camera Data","volume":"14","author":"Tan","year":"2014","journal-title":"Sensors"},{"key":"ref_3","unstructured":"Xu, W., Zhuang, Y., Hu, H., and Zhao, Y. (2014, January 27\u201330). Real-Time Road Detection and Description for Robot Navigation in an Unstructured Campus Environment. Proceeding of the 11th World Congress on Intelligent Control and Automation, Shenyang, China."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s10514-008-9091-x","article-title":"RoadCompass: Following rural roads with vision + ladar using vanishing point tracking","volume":"25","author":"Rasmussen","year":"2008","journal-title":"Auton Robot"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"He, Z., Wu, T., Xiao, Z., and He, H. (2013, January 15\u201318). Robust road detection from a single image using road shape prior. Proceedings of the 2013 IEEE International Conference on Image Processing (ICIP 2013), Melbourne, Australia.","DOI":"10.1109\/ICIP.2013.6738568"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s11554-008-0078-9","article-title":"Color image segmentation in HSI space for automotive applications","volume":"3","author":"Rotaru","year":"2008","journal-title":"J. Real Time Image Proc."},{"key":"ref_7","unstructured":"Christopher, R. (2002, January 11\u201315). Combining laser range, color, and texture cues for autonomous road following. Proceedings of the 2002 IEEE International Conference on Robotics and Automation (ICRA 2002), Washington, DC, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/TITS.2004.838221","article-title":"Color-based road detection in urban traffic scenes","volume":"5","author":"He","year":"2004","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1109\/TIP.2010.2045715","article-title":"General road detection from a single image","volume":"19","author":"Kong","year":"2010","journal-title":"IEEE Trans. Image Proc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/TITS.2010.2076349","article-title":"Road detection based on illuminant invariance","volume":"12","year":"2011","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1109\/TITS.2013.2295427","article-title":"Combining Priors, Appearance, and Context for Road Detection","volume":"15","author":"Gevers","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/TITS.2004.828175","article-title":"VIRTUOUS: Vision-Based Road Transportation for Unmanned Operation on Urban-Like Scenarios","volume":"5","author":"Sotelo","year":"2004","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_13","unstructured":"Vezhnevets, V., and Konouchine, V. (2005, January 20\u201324). \u201cGrowCut\u201d\u2014Interactive multi-label n-d image segmentation by cellular automata. Proceedings of the Fifteenth International Conference (GraphiCon\u20192005), Novosibirsk Akademgorodok, Russia."},{"key":"ref_14","unstructured":"Neumann, J.V., and Burks, A.W. (1966). Theory of Self-Reproducing Automata, University of Illinois Press."},{"key":"ref_15","unstructured":"He, X., Zemel, R., and Carreira-Perpinan, M. (July, January 27). Multiscale conditional random fields for image labeling. Proceedings of the 2004 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2004), Washington, DC, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s10514-008-9091-x","article-title":"Following rural roads with vision + ladar using vanishing point tracking","volume":"25","author":"Rasmussen","year":"2008","journal-title":"Auton Robot"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/TIP.2011.2162422","article-title":"Fast vanishing-point detection in unstructured environments","volume":"21","author":"Moghadam","year":"2012","journal-title":"IEEE Trans. Image Proc."},{"key":"ref_18","first-page":"301","article-title":"Studies on Three Modern Optimization Algorithms","volume":"27","author":"Hui","year":"2014","journal-title":"Value Eng."},{"key":"ref_19","unstructured":"Holland, J.H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, Y., Maybank, S.J., and Liang, J. (2014, January 9\u201312). A multi-modal moving object detection method based on GrowCut segmentation. Proceedings of the 2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, Orlando, FL, USA.","DOI":"10.1109\/CIMSIVP.2014.7013295"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"16128","DOI":"10.3390\/s140916128","article-title":"White Blood Cell Segmentation by Color-Space-Based K-Means Clustering","volume":"14","author":"Zhang","year":"2014","journal-title":"Sensors"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Veksler, O., Boykov, Y., and Mehrani, P. (2010, January 5\u201311). Superpixels and Supervoxels in an Energy Optimization Framework. Proceedings of the 11th European Conference on Computer Vision, Crete, Greece.","DOI":"10.1007\/978-3-642-15555-0_16"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","unstructured":"Nguyen, T.V., Tran, T., Vo, P., and Le, B. (2011, January 6\u201318). Efficient Image Segmentation Incorporating Photometric and Geometric Information. Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong, China."},{"key":"ref_25","unstructured":"Ara., N., and Gary, B. (2006, January 8\u201311). Detection of Drivable Corridors for Off-road Autonomous Navigation. Proceedings of the IEEE International Conference on Image Processing (ICIP 2006), Atlanta, GA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Veksler, O. (2008, January 12\u201318). Star shape prior for graph-cut image segmentation. Proceedings of the 10th European Conference on Computer Vision (ECCV 2008), Marseille, France.","DOI":"10.1007\/978-3-540-88690-7_34"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szummer, M., Kohli, P., and Hoiem, D. (2008, January 12\u201318). Learning CRFs Using Graph Cuts. Proceedings of the 10th European Conference on Computer Vision (ECCV 2008), Marseille, France.","DOI":"10.1007\/978-3-540-88688-4_43"},{"key":"ref_28","unstructured":"Shang, E., Zhao, H., Li, J., An, X., and Wu, T. Centre for Intelligent Machines. Available online: http:\/\/www.cim.mcgill.ca\/lijian\/roaddatabase.htm."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/11\/29594\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:52:37Z","timestamp":1760215957000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/11\/29594"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,24]]},"references-count":28,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2015,11]]}},"alternative-id":["s151129594"],"URL":"https:\/\/doi.org\/10.3390\/s151129594","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,11,24]]}}}