{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:06:46Z","timestamp":1760242006294,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,3]],"date-time":"2018-12-03T00:00:00Z","timestamp":1543795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the General Program of National Natural Science Foundation of China","award":["11702035","61603057"],"award-info":[{"award-number":["11702035","61603057"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["300102328403"],"award-info":[{"award-number":["300102328403"]}]},{"name":"the Shaanxi province Science and Technology Industrial Research Projects","award":["2015GY033"],"award-info":[{"award-number":["2015GY033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Shadows and normal light illumination and road and non-road areas are two pairs of contradictory symmetrical individuals. To achieve accurate road detection, it is necessary to remove interference caused by uneven illumination, such as shadows. This paper proposes a road detection algorithm based on a learning and illumination-independent image to solve the following problems: First, most road detection methods are sensitive to variation of illumination. Second, with traditional road detection methods based on illumination invariability, it is difficult to determine the calibration angle of the camera axis, and the sampling of road samples can be distorted. The proposed method contains three stages: The establishment of a classifier, the online capturing of an illumination-independent image, and the road detection. During the establishment of a classifier, a support vector machine (SVM) classifier for the road block is generated through training with the multi-feature fusion method. During the online capturing of an illumination-independent image, the road interest region is obtained by using a cascaded Hough transform parameterized by a parallel coordinate system. Five road blocks are obtained through the SVM classifier, and the RGB (Red, Green, Blue) space of the combined road blocks is converted to a geometric mean log chromatic space. Next, the camera axis calibration angle for each frame is determined according to the Shannon entropy so that the illumination-independent image of the respective frame is obtained. During the road detection, road sample points are extracted with the random sampling method. A confidence interval classifier of the road is established, which could separate a road from its background. This paper is based on public datasets and video sequences, which records roads of Chinese cities, suburbs, and schools in different traffic scenes. The author compares the method proposed in this paper with other sound video-based road detection methods and the results show that the method proposed in this paper can achieve a desired detection result with high quality and robustness. Meanwhile, the whole detection system can meet the real-time processing requirement.<\/jats:p>","DOI":"10.3390\/sym10120707","type":"journal-article","created":{"date-parts":[[2018,12,4]],"date-time":"2018-12-04T03:01:37Z","timestamp":1543892497000},"page":"707","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Online Road Detection under a Shadowy Traffic Image Using a Learning-Based Illumination-Independent Image"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5737-368X","authenticated-orcid":false,"given":"Yongchao","family":"Song","sequence":"first","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongfeng","family":"Ju","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Du","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2503-4525","authenticated-orcid":false,"given":"Weiyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiacheng","family":"Song","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1038\/497181a","article-title":"Sustainable mobility: A vision of our transport future","volume":"497","author":"Burns","year":"2013","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Islam, K.T., Raj, R.G., and Mujtaba, G. (2017). Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network. Symmetry, 9.","DOI":"10.3390\/sym9080138"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1109\/TITS.2013.2266661","article-title":"Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis","volume":"14","author":"Sivaraman","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/JAS.2018.7511063","article-title":"Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision","volume":"5","author":"Xing","year":"2018","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s00138-011-0404-2","article-title":"Recent progress in road and lane detection: A survey","volume":"25","author":"Hillel","year":"2014","journal-title":"Mach. Vis. Appl."},{"key":"ref_6","unstructured":"Mendes, C.C.T., Fr\u00e9mont, V., and Wolf, D.F. (arXiv, 2015). Vision-Based Road Detection using Contextual Blocks, arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2176","DOI":"10.1109\/TIP.2018.2792910","article-title":"When Dijkstra Meets Vanishing Point: A Stereo Vision Approach for Road Detection","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","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 Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Munajat, M.D.E., Widyantoro, D.H., and Munir, R. (2015, January 10\u201311). Road detection system based on RGB histogram filterization and boundary classifier. Proceedings of the International Conference on Advanced Computer Science and Information Systems, Depok, Indonesia.","DOI":"10.1109\/ICACSIS.2015.7415163"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Somawirata, I.K., and Utaminingrum, F. (2016, January 13\u201315). Road detection based on the color space and cluster connecting. Proceedings of the IEEE International Conference on Signal and Image Processing, Beijing, China.","DOI":"10.1109\/SIPROCESS.2016.7888235"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"29594","DOI":"10.3390\/s151129594","article-title":"Vision Sensor-Based Road Detection for Field Robot Navigation","volume":"15","author":"Lu","year":"2015","journal-title":"Sensors"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"0715001","DOI":"10.3788\/AOS201535.0715001","article-title":"An Efficient Road Detection Algorithm Based on Parallel Edges","volume":"35","author":"Wang","year":"2015","journal-title":"Acta Opt. Sin."},{"key":"ref_13","first-page":"81","article-title":"Road detection algorithm using the edge and region features in images","volume":"43","author":"Yang","year":"2013","journal-title":"J. Southeast Univ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhu, D., Dai, L., Luo, Y., Zhang, G., Shao, X., Itti, L., and Lu, J. (2018). Multi-Scale Adversarial Feature Learning for Saliency Detection. Symmetry, 10.","DOI":"10.3390\/sym10100457"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1109\/TGRS.2017.2669341","article-title":"Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network","volume":"55","author":"Cheng","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Narayan, A., Tuci, E., Labrosse, F., Alkilabi, M.H.M., Narayan, A., Tuci, E., Labrosse, F., and Alkilabi, M.H.M. (2017, January 4\u20138). Road detection using convolutional neural networks. Proceedings of the European Conference on Artificial Life Ecal, Lyon, France.","DOI":"10.7551\/ecal_a_053"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.compeleceng.2017.11.026","article-title":"Combining CNN and MRF for Road Detection","volume":"70","author":"Geng","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1109\/LSP.2018.2809685","article-title":"Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks","volume":"25","author":"Han","year":"2018","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Costea, A.D., and Nedevschi, S. (2017, January 11\u201314). Traffic scene segmentation based on boosting over multimodal low, intermediate and high order multi-range channel features. Proceedings of the Intelligent Vehicles Symposium, Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995701"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/TITS.2010.2076349","article-title":"Road Detection Based on Illuminant Invariance","volume":"12","author":"Alvarez","year":"2011","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, B., and Fr\u00e9mont, V. (2013, January 23\u201326). Fast road detection from color images. Proceedings of the Intelligent Vehicles Symposium, Gold Coast, QLD, Australia.","DOI":"10.1109\/IVS.2013.6629631"},{"key":"ref_23","first-page":"45","article-title":"Improved Road Detection Algorithm Based on Illuminant Invariant","volume":"17","author":"Kai","year":"2017","journal-title":"J. Transp. Syst. Eng. Inf. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Finlayson, G.D., Drew, M.S., and Lu, C. (2004, January 11\u201314). Intrinsic Images by Entropy Minimization. Proceedings of the European Conference on Computer Vision, Prague, Czech Republic.","DOI":"10.1007\/978-3-540-24672-5_46"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.cam.2006.11.001","article-title":"Nonlinear trigonometric approximation and the Dirac delta function","volume":"209","author":"Xu","year":"2007","journal-title":"J. Comput. Appl. Math."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neucom.2012.09.042","article-title":"Multi-scale gray level co-occurrence matrices for texture description","volume":"120","author":"Siqueira","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_28","first-page":"43","article-title":"Traffic sign detection based on Gaussian color model and SVM","volume":"35","author":"Chang","year":"2014","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dubsk\u00e1, M. (2013, January 9\u201313). Real Projective Plane Mapping for Detection of Orthogonal Vanishing Points. Proceedings of the British Machine Vision Conference, Bristol, UK.","DOI":"10.5244\/C.27.90"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fritsch, J., Kuhnl, T., and Geiger, A. (2013, January 6\u20139). A new performance measure and evaluation benchmark for road detection algorithms. Proceedings of the International IEEE Conference on Intelligent Transportation Systems, The Hague, Netherlands.","DOI":"10.1109\/ITSC.2013.6728473"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/12\/707\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:30:54Z","timestamp":1760196654000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/12\/707"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,3]]},"references-count":31,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["sym10120707"],"URL":"https:\/\/doi.org\/10.3390\/sym10120707","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2018,12,3]]}}}