{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T17:07:50Z","timestamp":1769360870138,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No.2018YFB0204301"],"award-info":[{"award-number":["No.2018YFB0204301"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["No.61872374"],"award-info":[{"award-number":["No.61872374"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>It is desirable to maintain high accuracy and runtime efficiency at the same time in lane detection. However, due to the long and thin properties of lanes, extracting features with both strong discrimination and perception abilities needs a huge amount of calculation, which seriously slows down the running speed. Therefore, we design a more efficient way to extract the features of lanes, including two phases: (1) Local feature extraction, which sets a series of predefined anchor lines, and extracts the local features through their locations. (2) Global feature aggregation, which treats local features as the nodes of the graph, and builds a fully connected graph by adaptively learning the distance between nodes, the global feature can be aggregated through weighted summing finally. Another problem that limits the performance is the information loss in feature compression, mainly due to the huge dimensional gap, e.g., from 512 to 8. To handle this issue, we propose a feature compression module based on decoupling representation learning. This module can effectively learn the statistical information and spatial relationships between features. After that, redundancy is greatly reduced and more critical information is retained. Extensional experimental results show that our proposed method is both fast and accurate. On the Tusimple and CULane benchmarks, with a running speed of 248 FPS, F1 values of 96.81% and 75.49% were achieved, respectively.<\/jats:p>","DOI":"10.3390\/s21144657","type":"journal-article","created":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T12:31:25Z","timestamp":1625661085000},"page":"4657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning"],"prefix":"10.3390","volume":"21","author":[{"given":"Yulin","family":"He","sequence":"first","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1641-5713","authenticated-orcid":false,"given":"Xin","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Libo","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Liu, G., W\u00f6rg\u00f6tter, F., and Markeli\u0107, I. (2010, January 21\u201324). Combining statistical hough transform and particle filter for robust lane detection and tracking. Proceedings of the 2010 IEEE Intelligent Vehicles Symposium, La Jolla, CA, USA.","DOI":"10.1109\/IVS.2010.5548021"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhou, S., Jiang, Y., Xi, J., Gong, J., Xiong, G., and Chen, H. (2010, January 21\u201324). A novel lane detection based on geometrical model and gabor filter. Proceedings of the 2010 IEEE Intelligent Vehicles Symposium, La Jolla, CA, USA.","DOI":"10.1109\/IVS.2010.5548087"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hur, J., Kang, S.N., and Seo, S.W. (2013, January 23\u201326). Multi-lane detection in urban driving environments using conditional random fields. Proceedings of the 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, QLD, Australia.","DOI":"10.1109\/IVS.2013.6629645"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TITS.2007.908582","article-title":"Robust lane detection and tracking in challenging scenarios","volume":"9","author":"Kim","year":"2008","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jiang, R., Klette, R., Vaudrey, T., and Wang, S. (2009, January 2\u20134). New lane model and distance transform for lane detection and tracking. Proceedings of the International Conference on Computer Analysis of Images and Patterns, M\u00fcnster, Germany.","DOI":"10.1007\/978-3-642-03767-2_127"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Daigavane, P.M., and Bajaj, P.R. (2010, January 19\u201321). Road lane detection with improved canny edges using ant colony optimization. Proceedings of the 2010 3rd International Conference on Emerging Trends in Engineering and Technology, Goa, India.","DOI":"10.1109\/ICETET.2010.128"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1177\/154193120504902217","article-title":"Using support vector machines for lane-change detection","volume":"Volume 49","author":"Mandalia","year":"2005","journal-title":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Neven, D., De Brabandere, B., Georgoulis, S., Proesmans, M., and Van Gool, L. (2018, January 26\u201330). Towards end-to-end lane detection: An instance segmentation approach. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500547"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pan, X., Shi, J., Luo, P., Wang, X., and Tang, X. (2018, January 2\u20137). Spatial as deep: Spatial cnn for traffic scene understanding. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"ref_11","unstructured":"Ko, Y., Jun, J., Ko, D., and Jeon, M. (2020). Key points estimation and point instance segmentation approach for lane detection. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hou, Y., Ma, Z., Liu, C., and Loy, C.C. (2019, January 27\u201328). Learning lightweight lane detection cnns by self attention distillation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00110"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lee, S., Kim, J., Shin Yoon, J., Shin, S., Bailo, O., Kim, N., Lee, T.H., Seok Hong, H., Han, S.H., and So Kweon, I. (2017, January 22\u201329). Vpgnet: Vanishing point guided network for lane and road marking detection and recognition. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.215"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hou, Y., Ma, Z., Liu, C., Hui, T.W., and Loy, C.C. (2020, January 14\u201319). Inter-region affinity distillation for road marking segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01250"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yoo, S., Lee, H.S., Myeong, H., Yun, S., Park, H., Cho, J., and Kim, D.H. (2020, January 14\u201319). End-to-end lane marker detection via row-wise classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00511"},{"key":"ref_16","unstructured":"Tabelini, L., Berriel, R., Paixao, T.M., Badue, C., De Souza, A.F., and Oliveira-Santos, T. (2020). PolyLaneNet: Lane estimation via deep polynomial regression. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1109\/TITS.2019.2890870","article-title":"Line-CNN: End-to-End Traffic line detection with line proposal unit","volume":"21","author":"Li","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, R., Yuan, Z., Liu, T., and Xiong, Z. (2021, January 5\u20139). End-to-end lane shape prediction with transformers. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00374"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Qin, Z., Wang, H., and Li, X. (2020). Ultra fast structure-aware deep lane detection. arXiv.","DOI":"10.1007\/978-3-030-58586-0_17"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, Z., Liu, Q., and Lian, C. (2019, January 9\u201312). PointLaneNet: Efficient end-to-end CNNs for Accurate Real-Time Lane Detection. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8813778"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, W., Liu, Z., Yu, Z., Dai, B., Lin, R., Wang, Y., Rehg, J.M., and Song, L. (2018, January 18\u201323). Decoupled networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00293"},{"key":"ref_22","unstructured":"Ma, J., Cui, P., Kuang, K., Wang, X., and Zhu, W. (2019, January 10\u201315). Disentangled graph convolutional networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_23","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_24","unstructured":"TuSimple (2017, July 30). Tusimple Benchmark. Available online: https:\/\/github.com\/TuSimple\/tusimple-benchmark."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bell, S., Zitnick, C.L., Bala, K., and Girshick, R. (2016, January 27\u201330). Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.314"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liang, X., Shen, X., Feng, J., Lin, L., and Yan, S. (2016, January 11\u201314). Semantic object parsing with graph lstm. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_8"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chougule, S., Koznek, N., Ismail, A., Adam, G., Narayan, V., and Schulze, M. (2018, January 4\u201318). Reliable multilane detection and classification by utilizing CNN as a regression network. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_46"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Philion, J. (2019, January 15\u201320). FastDraw: Addressing the long tail of lane detection by adapting a sequential prediction network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01185"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"De Brabandere, B., Neven, D., and Van Gool, L. (2017). Semantic instance segmentation with a discriminative loss function. arXiv.","DOI":"10.1109\/CVPRW.2017.66"},{"key":"ref_30","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. arXiv."},{"key":"ref_31","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 8\u201316). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Song, G., Liu, Y., and Wang, X. (2020, January 14\u201319). Revisiting the sibling head in object detector. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01158"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wu, Y., Chen, Y., Yuan, L., Liu, Z., Wang, L., Li, H., and Fu, Y. (2020, January 14\u201319). Rethinking classification and localization for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01020"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jiang, B., Luo, R., Mao, J., Xiao, T., and Jiang, Y. (2018, January 4\u201318). Acquisition of localization confidence for accurate object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_38","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, T., Chen, Z., Yang, Y., Wu, Z., and Li, H. (November, January 19). Lane detection in low-light conditions using an efficient data enhancement: Light conditions style transfer. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.","DOI":"10.1109\/IV47402.2020.9304613"},{"key":"ref_40","unstructured":"Li, Z. (2016, January 23\u201328). CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending. Proceedings of the European Conference on Computer Vision, Glasgow, UK."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4657\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:27:21Z","timestamp":1760164041000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4657"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,7]]},"references-count":40,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21144657"],"URL":"https:\/\/doi.org\/10.3390\/s21144657","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,7]]}}}