{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T12:24:16Z","timestamp":1784031856675,"version":"3.55.0"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T00:00:00Z","timestamp":1738022400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T00:00:00Z","timestamp":1738022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62073078"],"award-info":[{"award-number":["62073078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62073078"],"award-info":[{"award-number":["62073078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62073078"],"award-info":[{"award-number":["62073078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62073078"],"award-info":[{"award-number":["62073078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62073078"],"award-info":[{"award-number":["62073078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s11760-025-03842-0","type":"journal-article","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T18:39:27Z","timestamp":1738089567000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["AFLaneNet: an attention-fused instance segmentation network for real-time lane detection"],"prefix":"10.1007","volume":"19","author":[{"given":"Yafei","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shangzhe","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyi","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojie","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoguo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,28]]},"reference":[{"key":"3842_CR1","doi-asserted-by":"crossref","unstructured":"Zheng, T., Huang, Y., Liu, Y., Tang, W., Yang, Z., Cai, D., He, X.: CLRNET: Cross layer refinement network for lane detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 898\u2013907 (2022)","DOI":"10.1109\/CVPR52688.2022.00097"},{"key":"3842_CR2","doi-asserted-by":"crossref","unstructured":"Wen, Y., Yin, Y., Ran, H.: Flipnet: An attention-enhanced hierarchical feature flip fusion network for lane detection. IEEE Trans. Intell. Transp. Syst. (2024)","DOI":"10.1109\/TITS.2024.3380077"},{"key":"3842_CR3","doi-asserted-by":"crossref","unstructured":"Lee, M., Lee, J., Lee, D., Kim, W., Hwang, S., Lee, S.: Robust lane detection via expanded self attention. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 533\u2013542 (2022)","DOI":"10.1109\/WACV51458.2022.00201"},{"key":"3842_CR4","doi-asserted-by":"crossref","unstructured":"Zheng, T., Fang, H., Zhang, Y., Tang, W., Yang, Z., Liu, H., Cai, D.: Resa: Recurrent feature-shift aggregator for lane detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3547\u20133554 (2021)","DOI":"10.1609\/aaai.v35i4.16469"},{"key":"3842_CR5","doi-asserted-by":"crossref","unstructured":"Zhao, J., Qiu, Z., Hu, H., Sun, S.: Hwlane: HW-transformer for lane detection. IEEE Trans. Intell. Transp. Syst. (2024)","DOI":"10.1109\/TITS.2024.3386531"},{"issue":"21","key":"3842_CR6","doi-asserted-by":"publisher","first-page":"30685","DOI":"10.1007\/s11042-022-11940-1","volume":"81","author":"Y Luo","year":"2022","unstructured":"Luo, Y., Cao, X., Zhang, J., Guo, J., Shen, H., Wang, T., Feng, Q.: CE-FPN: Enhancing channel information for object detection. Multimed. Tools Appl. 81(21), 30685\u201330704 (2022)","journal-title":"Multimed. Tools Appl."},{"key":"3842_CR7","doi-asserted-by":"crossref","unstructured":"Pan, X., Shi, J., Luo, P., Wang, X., Tang, X.: Spatial as deep: Spatial CNN for traffic scene understanding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"3842_CR8","doi-asserted-by":"crossref","unstructured":"Hou, Y., Ma, Z., Liu, C., Loy, C.C.: Learning lightweight lane detection CNNS by self attention distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1013\u20131021 (2019)","DOI":"10.1109\/ICCV.2019.00110"},{"key":"3842_CR9","doi-asserted-by":"crossref","unstructured":"Qin, Z., Wang, H., Li, X.: Ultra fast structure-aware deep lane detection. In: European Conference on Computer Vision, pp. 276\u2013291 (2020). Springer","DOI":"10.1007\/978-3-030-58586-0_17"},{"key":"3842_CR10","doi-asserted-by":"crossref","unstructured":"Xu, H., Wang, S., Cai, X., Zhang, W., Liang, X., Li, Z.: Curvelane-NAS: Unifying lane-sensitive architecture search and adaptive point blending. In: European Conference on Computer Vision, pp. 689\u2013704 (2020). Springer","DOI":"10.1007\/978-3-030-58555-6_41"},{"issue":"1","key":"3842_CR11","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/TITS.2017.2750080","volume":"19","author":"E Romera","year":"2017","unstructured":"Romera, E., Alvarez, J.M., Bergasa, L.M., Arroyo, R.: ERFNET: Efficient residual factorized convnet for real-time semantic segmentation. IEEE Trans. Intell. Transp. Syst. 19(1), 263\u2013272 (2017)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"3842_CR12","doi-asserted-by":"crossref","unstructured":"Chiu, K.-Y., Lin, S.-F.: Lane detection using color-based segmentation. In: IEEE Proceedings. Intelligent Vehicles Symposium, 2005., pp. 706\u2013711 (2005). IEEE","DOI":"10.1109\/IVS.2005.1505186"},{"key":"3842_CR13","doi-asserted-by":"crossref","unstructured":"Tan, C., Hong, T., Chang, T., Shneier, M.: Color model-based real-time learning for road following. In: 2006 IEEE Intelligent Transportation Systems Conference, pp. 939\u2013944 (2006). IEEE","DOI":"10.1109\/ITSC.2006.1706865"},{"issue":"1","key":"3842_CR14","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1111\/mice.13051","volume":"39","author":"L Zhang","year":"2024","unstructured":"Zhang, L., Jiang, F., Yang, J., Kong, B., Hussain, A.: A real-time lane detection network using two-directional separation attention. Comput. Aided Civ. Infrastruct. Eng. 39(1), 86\u2013101 (2024)","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"3842_CR15","doi-asserted-by":"crossref","unstructured":"Tabelini, L., Berriel, R., Paixao, T.M., Badue, C., De\u00a0Souza, A.F., Oliveira-Santos, T.: Keep your eyes on the lane: Real-time attention-guided lane detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 294\u2013302 (2021)","DOI":"10.1109\/CVPR46437.2021.00036"},{"key":"3842_CR16","doi-asserted-by":"crossref","unstructured":"Xiao, L., Li, X., Yang, S., Yang, W.: Adnet: Lane shape prediction via anchor decomposition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6404\u20136413 (2023)","DOI":"10.1109\/ICCV51070.2023.00589"},{"key":"3842_CR17","unstructured":"Zhou, K., Zhou, R.: End-to-end lane detection with one-to-several transformer. arXiv preprint arXiv:2305.00675 (2023)"},{"key":"3842_CR18","doi-asserted-by":"crossref","unstructured":"Li, Q., Yu, X., Chen, J., He, B.-G., Wang, W., Rawat, D.B., Lyu, Z.: PGA-net: Polynomial global attention network with mean curvature loss for lane detection. IEEE Trans. Intell. Transp. Syst. (2023)","DOI":"10.1109\/TITS.2023.3309948"},{"key":"3842_CR19","doi-asserted-by":"crossref","unstructured":"Wang, J., Ma, Y., Huang, S., Hui, T., Wang, F., Qian, C., Zhang, T.: A keypoint-based global association network for lane detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1392\u20131401 (2022)","DOI":"10.1109\/CVPR52688.2022.00145"},{"key":"3842_CR20","doi-asserted-by":"crossref","unstructured":"Ko, Y., Lee, Y., Azam, S., Munir, F., Jeon, M., Pedrycz, W.: Key points estimation and point instance segmentation approach for lane detection. IEEE Trans. Intell. Transp. Syst. (2021)","DOI":"10.1109\/TITS.2021.3088488"},{"key":"3842_CR21","doi-asserted-by":"crossref","unstructured":"Bai, Y., Chen, Z., Fu, Z., Peng, L., Liang, P., Cheng, E.: Curveformer: 3d lane detection by curve propagation with curve queries and attention. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 7062\u20137068 (2023). IEEE","DOI":"10.1109\/ICRA48891.2023.10161160"},{"key":"3842_CR22","doi-asserted-by":"crossref","unstructured":"Liu, L., Chen, X., Zhu, S., Tan, P.: Condlanenet: A top-to-down lane detection framework based on conditional convolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3773\u20133782 (2021)","DOI":"10.1109\/ICCV48922.2021.00375"},{"issue":"1","key":"3842_CR23","doi-asserted-by":"publisher","first-page":"19193","DOI":"10.1038\/s41598-024-70116-z","volume":"14","author":"V Maddiralla","year":"2024","unstructured":"Maddiralla, V., Subramanian, S.: Effective lane detection on complex roads with convolutional attention mechanism in autonomous vehicles. Sci. Rep. 14(1), 19193 (2024)","journal-title":"Sci. Rep."},{"key":"3842_CR24","unstructured":"Qin, Z., Zhang, P., Li, X.: Ultra fast deep lane detection with hybrid anchor driven ordinal classification. IEEE Trans. Pattern Anal. Mach. Intell. (2022)"},{"key":"3842_CR25","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"3842_CR26","unstructured":"TuSimple: Tusimple benchmark. https:\/\/github.com\/TuSimple\/tusimple-benchmark (Accessed September, 2020)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-03842-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-03842-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-03842-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T14:55:25Z","timestamp":1739458525000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-03842-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,28]]},"references-count":26,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["3842"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-03842-0","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,28]]},"assertion":[{"value":"3 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The content of our submission is not applicable for human and\/ or animal studies.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"247"}}